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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

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Visualizing Time Series Data: 7 Types of Temporal Visualizations

What are some of the most common data visualizations you see in newspapers, textbooks, and corporate annual reports? Graphs showing a country’s GDP growth trends or charts capturing a company’s sales growth in the last 4 quarters would be high up on the list. Essentially, these are visualizations that track time series data — the performance of an indicator over a period of time — also known as temporal visualizations.

Temporal visualizations are one of the simplest, quickest ways to represent important time series data. In this blog, we have put together 7 handy temporal visualization styles for your time series data. Explore and let us know which is your favorite!

1. Line Graph

A line graph is the simplest way to represent time series data. It is intuitive, easy to create, and helps the viewer get a quick sense of how something has changed over time.

A line graph uses points connected by lines (also called trend lines) to show how a dependent variable and independent variable changed. An independent variable, true to its name, remains unaffected by other parameters, whereas the dependent variable depends on how the independent variable changes. For temporal visualizations, time is always the independent variable, which is plotted on the horizontal axis. Then the dependent variable is plotted on the vertical axis.

In the graph below, the populations of Europe and Ireland are the dependent variables and time is the independent variable.

This graph captures the population growth in Europe and Ireland from 1740 to around 2010. It clearly highlights the sudden drop in Ireland’s population in the 1840s. History books will tell you this was the result of the devastating Irish Potato Famine, a period of mass starvation, disease, and emigration in Ireland between 1845 and 1852.

Note that this graph uses different y-axis scales for its two dependent variables — the populations of Europe and Ireland. If the viewer doesn’t pay attention to the difference in the scales, they could be led to the conclusion that until about 1920, Ireland’s population was greater than that of Europe!

Use different scales with care and only when absolutely necessary. If you need to represent multiple variables on a line graph, try to use the same y-axis for all dependent variables to avoid confusion. If you can’t do this, like in the chart above, make sure both y-axes use the same number of increments and use color to show which y-axis belongs to which line.

As a good rule of thumb, don’t represent more than four variables on a line graph. With that many variables, the axis scales can become difficult to understand.

2. Stacked Area Chart

An area chart is similar to a line chart in that it has points connected by straight lines on a two-dimensional chart. It also puts time as the independent variable on the x-axis and the dependent variable on the y-axis. However, in an area chart, multiple variables are “stacked” on top of each other, and the area below each line is colored to represent each variable.

This is a stacked area chart showing time series data of student enrollments in India from 2001-10.

Stacked area charts are useful to show how both a cumulative total and individual components of that total changed over time.

The order in which we stack the variables is crucial because there can sometimes be a difference in the actual plot versus human perception . The chart plots the value vertically whereas we perceive the value to be at right angles to the general direction of the chart. For instance, in the case below, a bar graph would be a cleaner alternative.

3. Bar Charts

Bar charts represent data as horizontal or vertical bars. The length of each bar is proportional to the value of the variable at that point in time. A bar chart is the right choice for you when you wish to look at how the variable moved over time or when you wish to compare variables versus each other. Grouped or stacked bar charts help you combine both these purposes in one chart while keeping your visualization simple and intuitive.

For instance, this grouped bar chart in this interactive visualization of number of deaths by disease type in India not only lets you compare the deaths due to diarrhea, malaria, and acute respiratory disease across time, but also lets you compare the number of deaths by these three diseases in a given year.

By switching to the stacked bar chart view, you get an intuitive sense of the proportion of deaths caused by each disease.

To avoid clutter and confusion, make sure to not use more than 3 variables in a stacked or group bar chart. It is also a good practice to use consistent bold colors and leave appropriate space between two bars in a bar chart. Also, check out our blog on 5 common mistakes that lead to bad data visualization to learn why the base axis for your bar charts should start from zero.

4. Gantt Chart

A Gantt chart is a horizontal bar chart showing work completed in a certain period of time with respect to the time allocated for that particular task. It is named after the American engineer and management consultant Henry Gantt who extensively used this framework for project management.

Assume you’re planning the logistics for a dance concert. There are lots of activities to be completed, some of which will take place simultaneously while some can be done only after another activity has been completed. For instance, the choreographers, soundtrack, and dancers need to be finalized before the choreography can begin. However, the costumes, props, and stage decor can be planned at the same time as the choreography. With careful preparation, Gantt charts can help you plan for complex, long-term projects that are likely to undergo several revisions and have various resource and task dependencies.

Gantt charts are a popular project management tool since they present a concise snapshot of various tasks spread across various phases of the project. You can show additional information such as the correlation between individual tasks, resources used in each task, overlapping resources, etc., by the use of colors and placement of bars in a Gantt chart.

5. Stream Graph

A stream graph is essentially a stacked area graph, but displaced around a central horizontal axis. The stream graph looks like flowing liquid, hence the name.

Below is a stream graph showing a randomly chosen listener’s last.fm music-listening habits over time.

Stream graphs are great to represent and compare time series data for multiple variables. Stream graphs are, thus, apt for large data sets. Remember that choice of colors is very important, especially when there are lots of variables. Variables that do not have significantly high values might tend to get drowned out in the visualization if the colors are not chosen well.

6. Heat Map

Geospatial visualizations often use heat maps since they quickly help identify “hot spots” or regions of high concentrations of a given variable. When adapted to temporal visualizations, heat maps can help us explore two levels of time in a 2D array.

This heat map visualizes birthdays for babies born in the United States between 1973 and 1999. The vertical axis represents the 31 days in a month while the horizontal axis represents the 12 months in a year. This chart quickly helps us identify that a large number of babies were born in the later half of July, August, and September.

Heat maps are perfect for a two-tiered time frame — for instance, 7 days of the week spread across 52 weeks in the year, or 24 hours in a day spread across 30 days of the month, and so on. The limitation, though, is that only one variable can be visualized in a heat map. Comparison between two or more variables is very difficult to represent.

7. Polar Area Diagram

Think beyond the straight line! Sometimes, time series data can be cyclical — a season in a year, time of the day, and so on. Polar area diagrams help represent the cyclical nature time series data cleanly. A polar diagram looks like a traditional pie chart, but the sectors differ from each other not by the size of their angles but by how far they extend out from the centre of the circle.

This popular polar area diagram created by Florence Nightingale shows causes of mortality among British troops in the Crimean War. Each color in the diagram represents a different cause of death. (Check out the the text legend for more details.)

Polar area diagrams are useful for representing seasonal or cyclical time series data, such as climate or seasonal crop data. Multiple variables can be neatly stacked in the various sectors of the pie.

It is crucial to clarify whether the variable is proportional to the area or radius of the sector. It is a good practice to have the area of the sectors proportional to the value being represented. In that case, the radius should be proportional to the square root of the value of the variable (since area of a circle is proportional to the square of the radius).

Polar area diagrams, or pie charts in general, must be made with a lot of care to avoid misrepresentation. For more tips, check out this blog on 5 things you should know before you make a pie chart .

Go ahead… It’s “time” you made some cool temporal visualizations of your own!

Content at Atlan

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Great page. I love how you are thinking beyond line and area for visualization. The heatmaps got my attention. its a better way to present seasonality than series of lines for each year with X axis in months. i’ll try it out in d3 or other JS libraries. Good luck in your pursuits.

Data is king but visualization is the queen!

Thank you for this amazing detailed information about all the graphs.

What a great resource! Thank you for compiling these visualization options with such clear distinctions in optimal applications. Data allowing, I hope to find use for all of them.

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The phenomenology of time in interactive visual representations

Author : Carles Sora i Domenjó (Universitat Pompeu Fabra)

Citation: Sora i Domenjó, Carles (2010). "The phenomenology of time in interactive visual representations". Hipertext.net , 8, http://arxiu-web.upf.edu/hipertextnet/en/numero-8/time_interaction.html

Abstract: This article discusses the ways that time is represented in interactive images, and the ways that interactivity can be used to adjust both the understanding and representation of time.

Keywords: Time, metaphor, interactive design, cultural interfaces, tangible time, interactivity.

Table of contents

1. Introduction

Khronos projector, an interactive work of art by Alvaro Cassinelli [2005], is both the object of analysis and the point of departure for this article. The work has an unusual way of approaching the control of time with respect to images in motion [Figure 1]. A viewer of the artwork can control the passage of time in the image by pressing directly on the fabric where the images are projected, which accelerates or slows them. Time moves forward as a result of this action in different parts of the image, producing a composition of different tempos within the same image.

Figure 1. Cassinelli, Alvaro. http://www.k2.t.u-tokyo.ac.jp/members/alvaro/Khronos/

Cassinelli's work can be considered relevant because it contributes a new approach to conceptualizing visual representations of time, modifying the passage of time. In this work, depth is used as a dimension of time control in the images, exploring a new way of representing time that breaks with the classic convention of the "arrow of time".

In most visual representations, the march of time is normally presented as evolving in one dimension and in one direction, from left to right. What is proposed here is that some of the standard conventions could be revisited in the context of interactive communication. The nature of this medium could reformulate some cultural conventions, contributing new approaches in the same way that other media have reformulated them in the past. This happens with many cultural conventions and has also occurred with the visual representation of time. The current metaphor is a convention adopted de facto by Western culture that has seldom been contextualized, justified or analyzed in the digital context to date. It is the result of a "natural" adaptation to the medium. If we pay attention, we can find this convention in graphics in the print media, logic interfaces in video equipment, calendars, etc. [Hirotada, 1993]. We could ask ourselves: Could we represent time in other ways, in other dimensions"

Alvaro Cassinelli's project incites us to think about representations of time using other dimensions. The use of depth in this piece as a metaphor for control is uncommon in digital interfaces. Cultural conventions can adopt new forms depending on the context, and digital tools offer them a new space with creative friction.

The control dimension used in Khronos projector has not been very relevant in the design of digital interfaces until now [Buxton, 2007]. Using depth as a control dimension is specific to the interactive digital interfaces. No other medium exists through which we can control the dimension of time in an image by pressing with the hand directly on the projected image.

The fascination generated by some of the interfaces discussed in this article, with which we can modify time in a sequence of images, emerges from intrinsic motivations [Ribas, 2009] within the artistic interactive domain and from interaction design based on experimentation and exploration. In some cases, such as Khronos projector for example, these two factors create new modalities for interpreting and representing our culture.

We can also observe (albeit in isolated cases) examples of breaking the convention of representations of time in other disciplines, such as graphic design. Charles Joseph Minard's 1869 graphic, Napoleon's March [Figure 2], is one example. Up to 5 dimensions are presented within this one graphic, and time proceeds in two directions and is depicted in relationship to physical space [Tufte, 1983].

Figure 2. Tufte, Edward R. Visual explanations: images and quantities, evidence and narrative.

This article is an initial attempt to study the representation of time in interactive contexts using images in motion. Interactive communication contributes key elements to this study, but we cannot engage in the study until we first seek out all of the factors that gravitate to the structure of the concept of time, leaving aside for now physics and philosophy, topics that will have a fundamental role in this study in the future. What is presented here is a reflection on the representation of time and on its remediation within interactive media [Bolter, 2007].

2. Metaphors of space and time

Let us assume that we have a short film showing a sequence of images related in time but with an ambiguous sense of temporal direction. We can use the example of an object flying in the air without any other visual reference. In a film of this kind, it is impossible to determine whether the film is showing the original flight or is showing it in reverse. The arrow of time is not obvious in all processes.

Time, object of study in many disciplines, arts and sciences, attempts to describe the sequence of what happens in the world and of our physical experiences. However, some aspects of our daily experiences cannot be observed, measured or predicted. For example, does time move from left to right? Or perhaps from top to bottom? Those questions cannot be answered definitively because these are abstract concepts. Lakoff and Johnson suggest that humans construct their conceptual framework from a small group of concepts, based on experience, which serve to define all the other concepts that do not emerge directly from physical experience, and that these abstract concepts are best understood using metaphor [Lakoff and Johnson, 1980]. Therefore, in our everyday language the time domain is defined using spatial terms, a less abstract, more concrete domain, to define the more abstract one. In this case, temporal ideas are shaped by the knowledge of our spatial domain [Boroditsky, 2000].

With the goal of grasping the sequence of events, we generally conceive of time as a one-dimensional entity. Although in most languages the concept of time is expressed using spatial terminology (above, below, forward and back), visual representation applies a two-dimensional metaphor in the majority of cases, instead of a multi-dimensional representation [Clark, 1973]. There exists, then, in this domain shift (from abstract to concrete) a certain redefinition of the concept and a loss of meaning.

3. Learning, language and cultural contexts about time

Multiple factors intervene in the development of our knowledge of time: how we learn the concepts, in what language, and in what context.

When do we learn what time is? Our comprehension of the abstract concepts of time and space is the result of the structuring of language in childhood through direct experience. This structuring is limited and also shaped by the cultural context of the child.

Children acquire language primarily from external stimuli: what they hear and experience in the real world [Chomsky, 1965]. Throughout the process of language acquisition, the child creates knowledge through contact between cognitive mechanisms and the physical world: motor and sensory skills. The future assimilation of time and space is grounded in the cognitive knowledge developed during this phase [Clark, 1973].

The cultural context also has a fundamental role in the development of this knowledge, and language is a major determinant. In English, for example, the adverbs "before" and "after" (denoting a horizontal dimension) are used to refer to time. Curiously, in Mandarin Chinese these same words are used in general, but spatial metaphors with a vertical dimension are also used to refer to time [Skott, 1989]. Mandarin uses the adverbs up and down to distinguish between events closer or more distant in time (a vertical line dimension). What is relevant for our discussion is that there are important differences in the way Mandarin and English speakers think about time, according to the words they use for the spatial domain [Boroditsky, 2001]. This paradigm might exist in other languages.

Beyond the question of language, we need to acknowledge that the comprehension of the concepts of time and space are also enveloped in a historical and social context. For example, in some Andean regions, if someone asks a local how long it takes to walk from one place to another, the response might be a finite number of cigarettes. They use the cigarette (i.e., the time it takes to smoke one) as a unit of measure for distances and travel, which are spatial concepts [Steger, 1991].

From these two examples we can intuit that the understanding of time and its mediation using particular metaphors are subject to the cultural context. It follows, then, that they do not have the same meaning everywhere in the world.

Finally, another factor that appears to influence the construct of the perception of time and the manner of organizing events in time is the direction used for writing. This seems to offer a coherent explanation of the fact that the arrow of time differs so much for Mandarin and English speakers. We know that some Mandarin dialects are written from top to bottom and right to left, while English is written horizontally from left to right. Other variations in script exist. We can list other examples, such as a case in the Philippines of a language that is written from bottom to top. Or we can go back to the Greek alphabet, the origin of the left to right script, but which also made use of a bi-directional script called boustrophedon, in which alternate lines of text were read in a different direction (what we would consider "forward and backward").

Today we organize events sequentially by using various formats, directions and orientations. Films, comics, literature and paintings all have particular ways of articulating the concept of time. However, all of these time structures are conventions that were created at some point. We don't come into the world with a preconceived idea about time, space or motion. Consider, for example, some of the cave paintings in Altamira (Spain) that date from 18,000 years BC, in which the movement in the scenes follows no particular direction. Cave paintings have no time-structured narrative [Wachtel, 1993]. Having arrived at this point, we can affirm that the directionality of time in visual representations is a cultural convention.

4. Representation of movement

The representation of time and the study of motion has been a great challenge for scientists and artists throughout history. There are many cases in both art and science in which we can identify ad hoc inventions that have permitted the study of human beings through the study of movement and thereby coming close to a reflection about time [Cutting, 2002]. This is the case of Étienne Jules Marey, for example, and his photographic series published in La Machine Animale in 1873, in which he used a photographic revolver, designed expressly to capture movement, and other techniques such as stroboscopic images, the use of simultaneous photography to shoot different moments in time in the same space but from different perspectives, long exposures, or time lapse, used to accelerate time of some objects of study [Braun, 1992].

We can also find, in our contemporary artistic history, various attempts to codify space and time in a specific visual representation, with the goal of breaking the linear sequence of moving images. Futurist art, such as cubism or Dadaism, used two-dimensional representation of a scene within a plane. One of the best-known works that explored these ideas is Nu descendant un escalier (Nude descending a staircase), painted by Marcel Duchamp in 1912. Duchamp created a sequence of different moments within the same space from different points of view within a single canvas.

With the advent of cinema, the study of time took the form of a spatial perspective with the first opportunity to adjust the time of actual filming, through editing. This was an important moment for our study because, perhaps quite naturally, the first editing tables were organized from left to right, converting this decision into the standard for all analogue and digital video editing for all time, up to the present.

With the passage of time, cinema has intensified the relationship between time and image, creating new techniques such as bullet time, utilized in the 1999 Matrix film, which permitted navigation through space in a particular time.

Up until this point, these techniques had contributed new perspectives on our object of study, but were always used in the time of filming, never at the time of viewing. For that, it is necessary to permit the external participants to modify some of these dimensions. And so we arrive at interactive communication.

It is in the use of digital moving images controlled in real time that we can continue to formulate new relationships between the images and their temporal and spatial dimensions. Thanks to the digitalization of moving images, a participant can access any time point in the film: the time of filming, the time of editing, and the time of presentation. Of course, this always involves the use of the previously established metaphors for time and space. More recently, some research projects that use emerging digital technologies have facilitated access to these dimensions, utilizing new presentation strategies. The last clock [Figure 3], by Jussi Ängesleva and Ross Cooper [2001], is an autonomous application that captures images from a space with the goal of accumulating time in a two-dimensional plane in the form of a clock [Jaschko, S, 2002].

Figure 3. Ängesleva, Jussi; Cooper, Ross http://www.lastclock.co.uk/

In this visualization interface, the evolution of time in the images captured by a camera placed in the space is structured in a circular manner and with different degrees of resolution by hours, minutes and seconds. This is an alive, not static, representation, which places time within the parameters of the time that has passed, time of the recording. This is a singular dimension for our study.

In the field of interactivity we can differentiate between two types of applications: those that function with dynamic systems in an autonomous manner, without the participation of external actors, and those that are designed for someone to use them. In addition, within the second classification we could differentiate between projects that interest us (where the image is an object of study related to the phenomenon of time) according to their utilization of static analogue images, moving analogue images, digital moving images, and digital moving images modified in real time, or interactive images.

5. Final thoughts on digital interfaces

Although the emergence of some of these new technologies permits a re-ordering of the dimensions of time and space in video, we still need to establish new forms of interaction, perhaps outside the interface paradigm known as WIMP (window, icon, menu, pointer device), where we could take control of both dimensions using immersion interfaces. This is the case for Khronos projector, with which we began this article. It is reasonable to think that within a short time we will have access to new and more sophisticated techniques for interaction, such as for example the use of both hands on tactile surfaces [Fitzmaurice, 1995], using one as a pointer and the other as an anchor, which allows new means of representing the temporal metaphor, more meaningful interfaces that are more relevant to the creation of meaning [Backwell, 2006].

Multi-modality and the integration of controlled moving images in interactive media can open new approaches to the object of our study, e.g., conceptualizing time, for example in multiple directions or multiple instants in time, or utilizing physical gestures to stretch or shrink time. We would point out that the actions described here are in a different domain, other than spatial, and could give rise to new conventions in the representation of time, perhaps generating new metaphors not structured within the spatial domain: a liberation of the representation of time.

It is difficult to evaluate applications that disrupt cultural conventions, which are strongly linked to our culture and therefore to our convictions. Even so, it is essential to propose this study and the development of the interactive applications that can exemplify it, with the intention of finding new paradigms of the visual representation of time and, with them, the convention of a need to observe the remediation of interactive communication more closely.

In the future, it will be interesting to apply this methodology to other cultural conventions adopted in our visual culture and observe whether they can also be reformulated in interactive communication. The digital interfaces with which we design and build interactive applications are constantly evolving, and therefore this is an ongoing project that could contribute interesting new approaches to thinking about culture.

6. Bibliography

Backwell, A. F. (2006). The reification of metaphor as a design tool. ACM transactions on Computer-Human Interaction, Vol. 13.

Bolter, D.,Grusin, R. (2000). Remediation: understanding new media. MIT Press.

Boroditsky, L. (2000). Metaphoric structuring: understanding time through spatial metaphors. Cognition 75, 1-28. Elsevier.

Boroditsky, L. (2001). Does language shape thought?: Mandarin and English speakers? Conceptions of Time. Cog. Psicology 43, 1-22.

Buxton, B. (2007). Theories, models and basic concepts. Chapter 7. Models and Thories.

Braun, M. (1992). Picturing Time: the work of Etienne-Jules Marey (1830- 1904). The University of Chicago Press, Chicago.

Cassinelli, A. and Ishikawa, M. (2005). Khronos projector. Emerging Technologies, SIGGRAPH.

Clark, H. H. (1973). Space, time, semantics and the child. In T.E. Moore. Cognitive development and the acquisition of language. New York: Academic Press.

Cutting, E. J. (2002). Representing motion in a static image: constraints and parallels in art, science, and popular culture. Perception.

Fitzmaurice , G. Ishii, H. Buxton, W. (1995). Bricks: laying the foundations for graspable user interfaces. Proceedings of the SIGCHI.

Gibson, J.J. (1986). The ecological approach to visual perception. Houghton Mifflin Company.

Hirotada Ueda (1993). Automatic Structure Visualization for Video Editing. Proceedings of the INTERACT '93.

Jaschko, S. (2002). Space-Time Correlations Focused in Film Objects and Interactive Video. ISEA Papers, Nagoya/Japan.

Lakoff, G. and Johnson, M. (1980). Metaphors we live by. Chicago, IL. University of Chicago Press.

Ribas, Joan Ignasi (2009). Integració de mitjans en el discurs interactiu: el cas de la difusió cultural. Quaderns del CACNº. 31-32. Convergència tecnològica i audiovisual.

Steger, F.H. (1991). La concepción de tiempo y espacio en el mundo andino. Ed. Vervuert Verlag, Latinoamérica student 18.

Tufte, Edward R. (1983). Visual explanations : images and quantities, evidence and narrative . Cheshire, Graphics Press, cop.

Wachtel E, (1993). The first picture show: Cinematic aspects of cave art. Leonardo 26. MIT Press.

• Open access
• Published: 19 July 2015

The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works

• Maria Evagorou 1 ,
• Sibel Erduran 2 &
• Terhi Mäntylä 3  

International Journal of STEM Education volume  2 , Article number:  11 ( 2015 ) Cite this article

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The use of visual representations (i.e., photographs, diagrams, models) has been part of science, and their use makes it possible for scientists to interact with and represent complex phenomena, not observable in other ways. Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects of scientific practices, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective.

This is a theoretical paper, and in order to argue about the role of visualization, we first present a case study, that of the discovery of the structure of DNA that highlights the epistemic components of visual information in science. The second case study focuses on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of the experimentation. Third, we trace a contemporary account from science focusing on the experimental practices and how reproducibility of experimental procedures can be reinforced through video data.

Conclusions

Our conclusions suggest that in teaching science, the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Furthermore, we suggest that is it essential to design curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication and reflect on these practices. Implications for teacher education include the need for teacher professional development programs to problematize the use of visual representations as epistemic objects that are part of scientific practices.

During the last decades, research and reform documents in science education across the world have been calling for an emphasis not only on the content but also on the processes of science (Bybee 2014 ; Eurydice 2012 ; Duschl and Bybee 2014 ; Osborne 2014 ; Schwartz et al. 2012 ), in order to make science accessible to the students and enable them to understand the epistemic foundation of science. Scientific practices, part of the process of science, are the cognitive and discursive activities that are targeted in science education to develop epistemic understanding and appreciation of the nature of science (Duschl et al. 2008 ) and have been the emphasis of recent reform documents in science education across the world (Achieve 2013 ; Eurydice 2012 ). With the term scientific practices, we refer to the processes that take place during scientific discoveries and include among others: asking questions, developing and using models, engaging in arguments, and constructing and communicating explanations (National Research Council 2012 ). The emphasis on scientific practices aims to move the teaching of science from knowledge to the understanding of the processes and the epistemic aspects of science. Additionally, by placing an emphasis on engaging students in scientific practices, we aim to help students acquire scientific knowledge in meaningful contexts that resemble the reality of scientific discoveries.

Despite a wealth of research in science education on visual representations, the emphasis of such research has mainly been on the conceptual understanding when using visual representations and less on visual representations as epistemic objects. In this paper, we argue that by positioning visual representations as epistemic objects, science education can bring a renewed focus on how visualization contributes to knowledge formation in science from the learners’ perspective. Specifically, the use of visual representations (i.e., photographs, diagrams, tables, charts) has been part of science and over the years has evolved with the new technologies (i.e., from drawings to advanced digital images and three dimensional models). Visualization makes it possible for scientists to interact with complex phenomena (Richards 2003 ), and they might convey important evidence not observable in other ways. Visual representations as a tool to support cognitive understanding in science have been studied extensively (i.e., Gilbert 2010 ; Wu and Shah 2004 ). Studies in science education have explored the use of images in science textbooks (i.e., Dimopoulos et al. 2003 ; Bungum 2008 ), students’ representations or models when doing science (i.e., Gilbert et al. 2008 ; Dori et al. 2003 ; Lehrer and Schauble 2012 ; Schwarz et al. 2009 ), and students’ images of science and scientists (i.e., Chambers 1983 ). Therefore, studies in the field of science education have been using the term visualization as “the formation of an internal representation from an external representation” (Gilbert et al. 2008 , p. 4) or as a tool for conceptual understanding for students.

In this paper, we do not refer to visualization as mental image, model, or presentation only (Gilbert et al. 2008 ; Philips et al. 2010 ) but instead focus on visual representations or visualization as epistemic objects. Specifically, we refer to visualization as a process for knowledge production and growth in science. In this respect, modeling is an aspect of visualization, but what we are focusing on with visualization is not on the use of model as a tool for cognitive understanding (Gilbert 2010 ; Wu and Shah 2004 ) but the on the process of modeling as a scientific practice which includes the construction and use of models, the use of other representations, the communication in the groups with the use of the visual representation, and the appreciation of the difficulties that the science phase in this process. Therefore, the purpose of this paper is to present through the history of science how visualization can be considered not only as a cognitive tool in science education but also as an epistemic object that can potentially support students to understand aspects of the nature of science.

Scientific practices and science education

According to the New Generation Science Standards (Achieve 2013 ), scientific practices refer to: asking questions and defining problems; developing and using models; planning and carrying out investigations; analyzing and interpreting data; using mathematical and computational thinking; constructing explanations and designing solutions; engaging in argument from evidence; and obtaining, evaluating, and communicating information. A significant aspect of scientific practices is that science learning is more than just about learning facts, concepts, theories, and laws. A fuller appreciation of science necessitates the understanding of the science relative to its epistemological grounding and the process that are involved in the production of knowledge (Hogan and Maglienti 2001 ; Wickman 2004 ).

The New Generation Science Standards is, among other changes, shifting away from science inquiry and towards the inclusion of scientific practices (Duschl and Bybee 2014 ; Osborne 2014 ). By comparing the abilities to do scientific inquiry (National Research Council 2000 ) with the set of scientific practices, it is evident that the latter is about engaging in the processes of doing science and experiencing in that way science in a more authentic way. Engaging in scientific practices according to Osborne ( 2014 ) “presents a more authentic picture of the endeavor that is science” (p.183) and also helps the students to develop a deeper understanding of the epistemic aspects of science. Furthermore, as Bybee ( 2014 ) argues, by engaging students in scientific practices, we involve them in an understanding of the nature of science and an understanding on the nature of scientific knowledge.

Science as a practice and scientific practices as a term emerged by the philosopher of science, Kuhn (Osborne 2014 ), refers to the processes in which the scientists engage during knowledge production and communication. The work that is followed by historians, philosophers, and sociologists of science (Latour 2011 ; Longino 2002 ; Nersessian 2008 ) revealed the scientific practices in which the scientists engage in and include among others theory development and specific ways of talking, modeling, and communicating the outcomes of science.

Visualization as an epistemic object

Schematic, pictorial symbols in the design of scientific instruments and analysis of the perceptual and functional information that is being stored in those images have been areas of investigation in philosophy of scientific experimentation (Gooding et al. 1993 ). The nature of visual perception, the relationship between thought and vision, and the role of reproducibility as a norm for experimental research form a central aspect of this domain of research in philosophy of science. For instance, Rothbart ( 1997 ) has argued that visualizations are commonplace in the theoretical sciences even if every scientific theory may not be defined by visualized models.

Visual representations (i.e., photographs, diagrams, tables, charts, models) have been used in science over the years to enable scientists to interact with complex phenomena (Richards 2003 ) and might convey important evidence not observable in other ways (Barber et al. 2006 ). Some authors (e.g., Ruivenkamp and Rip 2010 ) have argued that visualization is as a core activity of some scientific communities of practice (e.g., nanotechnology) while others (e.g., Lynch and Edgerton 1988 ) have differentiated the role of particular visualization techniques (e.g., of digital image processing in astronomy). Visualization in science includes the complex process through which scientists develop or produce imagery, schemes, and graphical representation, and therefore, what is of importance in this process is not only the result but also the methodology employed by the scientists, namely, how this result was produced. Visual representations in science may refer to objects that are believed to have some kind of material or physical existence but equally might refer to purely mental, conceptual, and abstract constructs (Pauwels 2006 ). More specifically, visual representations can be found for: (a) phenomena that are not observable with the eye (i.e., microscopic or macroscopic); (b) phenomena that do not exist as visual representations but can be translated as such (i.e., sound); and (c) in experimental settings to provide visual data representations (i.e., graphs presenting velocity of moving objects). Additionally, since science is not only about replicating reality but also about making it more understandable to people (either to the public or other scientists), visual representations are not only about reproducing the nature but also about: (a) functioning in helping solving a problem, (b) filling gaps in our knowledge, and (c) facilitating knowledge building or transfer (Lynch 2006 ).

Using or developing visual representations in the scientific practice can range from a straightforward to a complicated situation. More specifically, scientists can observe a phenomenon (i.e., mitosis) and represent it visually using a picture or diagram, which is quite straightforward. But they can also use a variety of complicated techniques (i.e., crystallography in the case of DNA studies) that are either available or need to be developed or refined in order to acquire the visual information that can be used in the process of theory development (i.e., Latour and Woolgar 1979 ). Furthermore, some visual representations need decoding, and the scientists need to learn how to read these images (i.e., radiologists); therefore, using visual representations in the process of science requires learning a new language that is specific to the medium/methods that is used (i.e., understanding an X-ray picture is different from understanding an MRI scan) and then communicating that language to other scientists and the public.

There are much intent and purposes of visual representations in scientific practices, as for example to make a diagnosis, compare, describe, and preserve for future study, verify and explore new territory, generate new data (Pauwels 2006 ), or present new methodologies. According to Latour and Woolgar ( 1979 ) and Knorr Cetina ( 1999 ), visual representations can be used either as primary data (i.e., image from a microscope). or can be used to help in concept development (i.e., models of DNA used by Watson and Crick), to uncover relationships and to make the abstract more concrete (graphs of sound waves). Therefore, visual representations and visual practices, in all forms, are an important aspect of the scientific practices in developing, clarifying, and transmitting scientific knowledge (Pauwels 2006 ).

Methods and Results: Merging Visualization and scientific practices in science

In this paper, we present three case studies that embody the working practices of scientists in an effort to present visualization as a scientific practice and present our argument about how visualization is a complex process that could include among others modeling and use of representation but is not only limited to that. The first case study explores the role of visualization in the construction of knowledge about the structure of DNA, using visuals as evidence. The second case study focuses on Faraday’s use of the lines of magnetic force and the visual reasoning leading to the theoretical development that was an inherent part of the experimentation. The third case study focuses on the current practices of scientists in the context of a peer-reviewed journal called the Journal of Visualized Experiments where the methodology is communicated through videotaped procedures. The three case studies represent the research interests of the three authors of this paper and were chosen to present how visualization as a practice can be involved in all stages of doing science, from hypothesizing and evaluating evidence (case study 1) to experimenting and reasoning (case study 2) to communicating the findings and methodology with the research community (case study 3), and represent in this way the three functions of visualization as presented by Lynch ( 2006 ). Furthermore, the last case study showcases how the development of visualization technologies has contributed to the communication of findings and methodologies in science and present in that way an aspect of current scientific practices. In all three cases, our approach is guided by the observation that the visual information is an integral part of scientific practices at the least and furthermore that they are particularly central in the scientific practices of science.

Case study 1: use visual representations as evidence in the discovery of DNA

The focus of the first case study is the discovery of the structure of DNA. The DNA was first isolated in 1869 by Friedrich Miescher, and by the late 1940s, it was known that it contained phosphate, sugar, and four nitrogen-containing chemical bases. However, no one had figured the structure of the DNA until Watson and Crick presented their model of DNA in 1953. Other than the social aspects of the discovery of the DNA, another important aspect was the role of visual evidence that led to knowledge development in the area. More specifically, by studying the personal accounts of Watson ( 1968 ) and Crick ( 1988 ) about the discovery of the structure of the DNA, the following main ideas regarding the role of visual representations in the production of knowledge can be identified: (a) The use of visual representations was an important part of knowledge growth and was often dependent upon the discovery of new technologies (i.e., better microscopes or better techniques in crystallography that would provide better visual representations as evidence of the helical structure of the DNA); and (b) Models (three-dimensional) were used as a way to represent the visual images (X-ray images) and connect them to the evidence provided by other sources to see whether the theory can be supported. Therefore, the model of DNA was built based on the combination of visual evidence and experimental data.

An example showcasing the importance of visual representations in the process of knowledge production in this case is provided by Watson, in his book The Double Helix (1968):

…since the middle of the summer Rosy [Rosalind Franklin] had had evidence for a new three-dimensional form of DNA. It occurred when the DNA 2molecules were surrounded by a large amount of water. When I asked what the pattern was like, Maurice went into the adjacent room to pick up a print of the new form they called the “B” structure. The instant I saw the picture, my mouth fell open and my pulse began to race. The pattern was unbelievably simpler than those previously obtained (A form). Moreover, the black cross of reflections which dominated the picture could arise only from a helical structure. With the A form the argument for the helix was never straightforward, and considerable ambiguity existed as to exactly which type of helical symmetry was present. With the B form however, mere inspection of its X-ray picture gave several of the vital helical parameters. (p. 167-169)

As suggested by Watson’s personal account of the discovery of the DNA, the photo taken by Rosalind Franklin (Fig.  1 ) convinced him that the DNA molecule must consist of two chains arranged in a paired helix, which resembles a spiral staircase or ladder, and on March 7, 1953, Watson and Crick finished and presented their model of the structure of DNA (Watson and Berry 2004 ; Watson 1968 ) which was based on the visual information provided by the X-ray image and their knowledge of chemistry.

X-ray chrystallography of DNA

In analyzing the visualization practice in this case study, we observe the following instances that highlight how the visual information played a role:

Asking questions and defining problems: The real world in the model of science can at some points only be observed through visual representations or representations, i.e., if we are using DNA as an example, the structure of DNA was only observable through the crystallography images produced by Rosalind Franklin in the laboratory. There was no other way to observe the structure of DNA, therefore the real world.

Analyzing and interpreting data: The images that resulted from crystallography as well as their interpretations served as the data for the scientists studying the structure of DNA.

Experimenting: The data in the form of visual information were used to predict the possible structure of the DNA.

Modeling: Based on the prediction, an actual three-dimensional model was prepared by Watson and Crick. The first model did not fit with the real world (refuted by Rosalind Franklin and her research group from King’s College) and Watson and Crick had to go through the same process again to find better visual evidence (better crystallography images) and create an improved visual model.

Example excerpts from Watson’s biography provide further evidence for how visualization practices were applied in the context of the discovery of DNA (Table  1 ).

In summary, by examining the history of the discovery of DNA, we showcased how visual data is used as scientific evidence in science, identifying in that way an aspect of the nature of science that is still unexplored in the history of science and an aspect that has been ignored in the teaching of science. Visual representations are used in many ways: as images, as models, as evidence to support or rebut a model, and as interpretations of reality.

Case study 2: applying visual reasoning in knowledge production, the example of the lines of magnetic force

The focus of this case study is on Faraday’s use of the lines of magnetic force. Faraday is known of his exploratory, creative, and yet systemic way of experimenting, and the visual reasoning leading to theoretical development was an inherent part of this experimentation (Gooding 2006 ). Faraday’s articles or notebooks do not include mathematical formulations; instead, they include images and illustrations from experimental devices and setups to the recapping of his theoretical ideas (Nersessian 2008 ). According to Gooding ( 2006 ), “Faraday’s visual method was designed not to copy apparent features of the world, but to analyse and replicate them” (2006, p. 46).

The lines of force played a central role in Faraday’s research on electricity and magnetism and in the development of his “field theory” (Faraday 1852a ; Nersessian 1984 ). Before Faraday, the experiments with iron filings around magnets were known and the term “magnetic curves” was used for the iron filing patterns and also for the geometrical constructs derived from the mathematical theory of magnetism (Gooding et al. 1993 ). However, Faraday used the lines of force for explaining his experimental observations and in constructing the theory of forces in magnetism and electricity. Examples of Faraday’s different illustrations of lines of magnetic force are given in Fig.  2 . Faraday gave the following experiment-based definition for the lines of magnetic forces:

a Iron filing pattern in case of bar magnet drawn by Faraday (Faraday 1852b , Plate IX, p. 158, Fig. 1), b Faraday’s drawing of lines of magnetic force in case of cylinder magnet, where the experimental procedure, knife blade showing the direction of lines, is combined into drawing (Faraday, 1855, vol. 1, plate 1)

A line of magnetic force may be defined as that line which is described by a very small magnetic needle, when it is so moved in either direction correspondent to its length, that the needle is constantly a tangent to the line of motion; or it is that line along which, if a transverse wire be moved in either direction, there is no tendency to the formation of any current in the wire, whilst if moved in any other direction there is such a tendency; or it is that line which coincides with the direction of the magnecrystallic axis of a crystal of bismuth, which is carried in either direction along it. The direction of these lines about and amongst magnets and electric currents, is easily represented and understood, in a general manner, by the ordinary use of iron filings. (Faraday 1852a , p. 25 (3071))

The definition describes the connection between the experiments and the visual representation of the results. Initially, the lines of force were just geometric representations, but later, Faraday treated them as physical objects (Nersessian 1984 ; Pocovi and Finlay 2002 ):

I have sometimes used the term lines of force so vaguely, as to leave the reader doubtful whether I intended it as a merely representative idea of the forces, or as the description of the path along which the power was continuously exerted. … wherever the expression line of force is taken simply to represent the disposition of forces, it shall have the fullness of that meaning; but that wherever it may seem to represent the idea of the physical mode of transmission of the force, it expresses in that respect the opinion to which I incline at present. The opinion may be erroneous, and yet all that relates or refers to the disposition of the force will remain the same. (Faraday, 1852a , p. 55-56 (3075))

He also felt that the lines of force had greater explanatory power than the dominant theory of action-at-a-distance:

Now it appears to me that these lines may be employed with great advantage to represent nature, condition, direction and comparative amount of the magnetic forces; and that in many cases they have, to the physical reasoned at least, a superiority over that method which represents the forces as concentrated in centres of action… (Faraday, 1852a , p. 26 (3074))

For giving some insight to Faraday’s visual reasoning as an epistemic practice, the following examples of Faraday’s studies of the lines of magnetic force (Faraday 1852a , 1852b ) are presented:

(a) Asking questions and defining problems: The iron filing patterns formed the empirical basis for the visual model: 2D visualization of lines of magnetic force as presented in Fig.  2 . According to Faraday, these iron filing patterns were suitable for illustrating the direction and form of the magnetic lines of force (emphasis added):

It must be well understood that these forms give no indication by their appearance of the relative strength of the magnetic force at different places, inasmuch as the appearance of the lines depends greatly upon the quantity of filings and the amount of tapping; but the direction and forms of these lines are well given, and these indicate, in a considerable degree, the direction in which the forces increase and diminish . (Faraday 1852b , p.158 (3237))

Despite being static and two dimensional on paper, the lines of magnetic force were dynamical (Nersessian 1992 , 2008 ) and three dimensional for Faraday (see Fig.  2 b). For instance, Faraday described the lines of force “expanding”, “bending,” and “being cut” (Nersessian 1992 ). In Fig.  2 b, Faraday has summarized his experiment (bar magnet and knife blade) and its results (lines of force) in one picture.

(b) Analyzing and interpreting data: The model was so powerful for Faraday that he ended up thinking them as physical objects (e.g., Nersessian 1984 ), i.e., making interpretations of the way forces act. Of course, he made a lot of experiments for showing the physical existence of the lines of force, but he did not succeed in it (Nersessian 1984 ). The following quote illuminates Faraday’s use of the lines of force in different situations:

The study of these lines has, at different times, been greatly influential in leading me to various results, which I think prove their utility as well as fertility. Thus, the law of magneto-electric induction; the earth’s inductive action; the relation of magnetism and light; diamagnetic action and its law, and magnetocrystallic action, are the cases of this kind… (Faraday 1852a , p. 55 (3174))

(c) Experimenting: In Faraday's case, he used a lot of exploratory experiments; in case of lines of magnetic force, he used, e.g., iron filings, magnetic needles, or current carrying wires (see the quote above). The magnetic field is not directly observable and the representation of lines of force was a visual model, which includes the direction, form, and magnitude of field.

(d) Modeling: There is no denying that the lines of magnetic force are visual by nature. Faraday’s views of lines of force developed gradually during the years, and he applied and developed them in different contexts such as electromagnetic, electrostatic, and magnetic induction (Nersessian 1984 ). An example of Faraday’s explanation of the effect of the wire b’s position to experiment is given in Fig.  3 . In Fig.  3 , few magnetic lines of force are drawn, and in the quote below, Faraday is explaining the effect using these magnetic lines of force (emphasis added):

Picture of an experiment with different arrangements of wires ( a , b’ , b” ), magnet, and galvanometer. Note the lines of force drawn around the magnet. (Faraday 1852a , p. 34)

It will be evident by inspection of Fig. 3 , that, however the wires are carried away, the general result will, according to the assumed principles of action, be the same; for if a be the axial wire, and b’, b”, b”’ the equatorial wire, represented in three different positions, whatever magnetic lines of force pass across the latter wire in one position, will also pass it in the other, or in any other position which can be given to it. The distance of the wire at the place of intersection with the lines of force, has been shown, by the experiments (3093.), to be unimportant. (Faraday 1852a , p. 34 (3099))

In summary, by examining the history of Faraday’s use of lines of force, we showed how visual imagery and reasoning played an important part in Faraday’s construction and representation of his “field theory”. As Gooding has stated, “many of Faraday’s sketches are far more that depictions of observation, they are tools for reasoning with and about phenomena” (2006, p. 59).

Case study 3: visualizing scientific methods, the case of a journal

The focus of the third case study is the Journal of Visualized Experiments (JoVE) , a peer-reviewed publication indexed in PubMed. The journal devoted to the publication of biological, medical, chemical, and physical research in a video format. The journal describes its history as follows:

JoVE was established as a new tool in life science publication and communication, with participation of scientists from leading research institutions. JoVE takes advantage of video technology to capture and transmit the multiple facets and intricacies of life science research. Visualization greatly facilitates the understanding and efficient reproduction of both basic and complex experimental techniques, thereby addressing two of the biggest challenges faced by today's life science research community: i) low transparency and poor reproducibility of biological experiments and ii) time and labor-intensive nature of learning new experimental techniques. ( http://www.jove.com/ )

By examining the journal content, we generate a set of categories that can be considered as indicators of relevance and significance in terms of epistemic practices of science that have relevance for science education. For example, the quote above illustrates how scientists view some norms of scientific practice including the norms of “transparency” and “reproducibility” of experimental methods and results, and how the visual format of the journal facilitates the implementation of these norms. “Reproducibility” can be considered as an epistemic criterion that sits at the heart of what counts as an experimental procedure in science:

Investigating what should be reproducible and by whom leads to different types of experimental reproducibility, which can be observed to play different roles in experimental practice. A successful application of the strategy of reproducing an experiment is an achievement that may depend on certain isiosyncratic aspects of a local situation. Yet a purely local experiment that cannot be carried out by other experimenters and in other experimental contexts will, in the end be unproductive in science. (Sarkar and Pfeifer 2006 , p.270)

We now turn to an article on “Elevated Plus Maze for Mice” that is available for free on the journal website ( http://www.jove.com/video/1088/elevated-plus-maze-for-mice ). The purpose of this experiment was to investigate anxiety levels in mice through behavioral analysis. The journal article consists of a 9-min video accompanied by text. The video illustrates the handling of the mice in soundproof location with less light, worksheets with characteristics of mice, computer software, apparatus, resources, setting up the computer software, and the video recording of mouse behavior on the computer. The authors describe the apparatus that is used in the experiment and state how procedural differences exist between research groups that lead to difficulties in the interpretation of results:

The apparatus consists of open arms and closed arms, crossed in the middle perpendicularly to each other, and a center area. Mice are given access to all of the arms and are allowed to move freely between them. The number of entries into the open arms and the time spent in the open arms are used as indices of open space-induced anxiety in mice. Unfortunately, the procedural differences that exist between laboratories make it difficult to duplicate and compare results among laboratories.

The authors’ emphasis on the particularity of procedural context echoes in the observations of some philosophers of science:

It is not just the knowledge of experimental objects and phenomena but also their actual existence and occurrence that prove to be dependent on specific, productive interventions by the experimenters” (Sarkar and Pfeifer 2006 , pp. 270-271)

The inclusion of a video of the experimental procedure specifies what the apparatus looks like (Fig.  4 ) and how the behavior of the mice is captured through video recording that feeds into a computer (Fig.  5 ). Subsequently, a computer software which captures different variables such as the distance traveled, the number of entries, and the time spent on each arm of the apparatus. Here, there is visual information at different levels of representation ranging from reconfiguration of raw video data to representations that analyze the data around the variables in question (Fig.  6 ). The practice of levels of visual representations is not particular to the biological sciences. For instance, they are commonplace in nanotechnological practices:

Visual illustration of apparatus

Video processing of experimental set-up

Computer software for video input and variable recording

In the visualization processes, instruments are needed that can register the nanoscale and provide raw data, which needs to be transformed into images. Some Imaging Techniques have software incorporated already where this transformation automatically takes place, providing raw images. Raw data must be translated through the use of Graphic Software and software is also used for the further manipulation of images to highlight what is of interest to capture the (inferred) phenomena -- and to capture the reader. There are two levels of choice: Scientists have to choose which imaging technique and embedded software to use for the job at hand, and they will then have to follow the structure of the software. Within such software, there are explicit choices for the scientists, e.g. about colour coding, and ways of sharpening images. (Ruivenkamp and Rip 2010 , pp.14–15)

On the text that accompanies the video, the authors highlight the role of visualization in their experiment:

Visualization of the protocol will promote better understanding of the details of the entire experimental procedure, allowing for standardization of the protocols used in different laboratories and comparisons of the behavioral phenotypes of various strains of mutant mice assessed using this test.

The software that takes the video data and transforms it into various representations allows the researchers to collect data on mouse behavior more reliably. For instance, the distance traveled across the arms of the apparatus or the time spent on each arm would have been difficult to observe and record precisely. A further aspect to note is how the visualization of the experiment facilitates control of bias. The authors illustrate how the olfactory bias between experimental procedures carried on mice in sequence is avoided by cleaning the equipment.

Our discussion highlights the role of visualization in science, particularly with respect to presenting visualization as part of the scientific practices. We have used case studies from the history of science highlighting a scientist’s account of how visualization played a role in the discovery of DNA and the magnetic field and from a contemporary illustration of a science journal’s practices in incorporating visualization as a way to communicate new findings and methodologies. Our implicit aim in drawing from these case studies was the need to align science education with scientific practices, particularly in terms of how visual representations, stable or dynamic, can engage students in the processes of science and not only to be used as tools for cognitive development in science. Our approach was guided by the notion of “knowledge-as-practice” as advanced by Knorr Cetina ( 1999 ) who studied scientists and characterized their knowledge as practice, a characterization which shifts focus away from ideas inside scientists’ minds to practices that are cultural and deeply contextualized within fields of science. She suggests that people working together can be examined as epistemic cultures whose collective knowledge exists as practice.

It is important to stress, however, that visual representations are not used in isolation, but are supported by other types of evidence as well, or other theories (i.e., in order to understand the helical form of DNA, or the structure, chemistry knowledge was needed). More importantly, this finding can also have implications when teaching science as argument (e.g., Erduran and Jimenez-Aleixandre 2008 ), since the verbal evidence used in the science classroom to maintain an argument could be supported by visual evidence (either a model, representation, image, graph, etc.). For example, in a group of students discussing the outcomes of an introduced species in an ecosystem, pictures of the species and the ecosystem over time, and videos showing the changes in the ecosystem, and the special characteristics of the different species could serve as visual evidence to help the students support their arguments (Evagorou et al. 2012 ). Therefore, an important implication for the teaching of science is the use of visual representations as evidence in the science curriculum as part of knowledge production. Even though studies in the area of science education have focused on the use of models and modeling as a way to support students in the learning of science (Dori et al. 2003 ; Lehrer and Schauble 2012 ; Mendonça and Justi 2013 ; Papaevripidou et al. 2007 ) or on the use of images (i.e., Korfiatis et al. 2003 ), with the term using visuals as evidence, we refer to the collection of all forms of visuals and the processes involved.

Another aspect that was identified through the case studies is that of the visual reasoning (an integral part of Faraday’s investigations). Both the verbalization and visualization were part of the process of generating new knowledge (Gooding 2006 ). Even today, most of the textbooks use the lines of force (or just field lines) as a geometrical representation of field, and the number of field lines is connected to the quantity of flux. Often, the textbooks use the same kind of visual imagery than in what is used by scientists. However, when using images, only certain aspects or features of the phenomena or data are captured or highlighted, and often in tacit ways. Especially in textbooks, the process of producing the image is not presented and instead only the product—image—is left. This could easily lead to an idea of images (i.e., photos, graphs, visual model) being just representations of knowledge and, in the worse case, misinterpreted representations of knowledge as the results of Pocovi and Finlay ( 2002 ) in case of electric field lines show. In order to avoid this, the teachers should be able to explain how the images are produced (what features of phenomena or data the images captures, on what ground the features are chosen to that image, and what features are omitted); in this way, the role of visualization in knowledge production can be made “visible” to students by engaging them in the process of visualization.

The implication of these norms for science teaching and learning is numerous. The classroom contexts can model the generation, sharing and evaluation of evidence, and experimental procedures carried out by students, thereby promoting not only some contemporary cultural norms in scientific practice but also enabling the learning of criteria, standards, and heuristics that scientists use in making decisions on scientific methods. As we have demonstrated with the three case studies, visual representations are part of the process of knowledge growth and communication in science, as demonstrated with two examples from the history of science and an example from current scientific practices. Additionally, visual information, especially with the use of technology is a part of students’ everyday lives. Therefore, we suggest making use of students’ knowledge and technological skills (i.e., how to produce their own videos showing their experimental method or how to identify or provide appropriate visual evidence for a given topic), in order to teach them the aspects of the nature of science that are often neglected both in the history of science and the design of curriculum. Specifically, what we suggest in this paper is that students should actively engage in visualization processes in order to appreciate the diverse nature of doing science and engage in authentic scientific practices.

However, as a word of caution, we need to distinguish the products and processes involved in visualization practices in science:

If one considers scientific representations and the ways in which they can foster or thwart our understanding, it is clear that a mere object approach, which would devote all attention to the representation as a free-standing product of scientific labor, is inadequate. What is needed is a process approach: each visual representation should be linked with its context of production (Pauwels 2006 , p.21).

The aforementioned suggests that the emphasis in visualization should shift from cognitive understanding—using the products of science to understand the content—to engaging in the processes of visualization. Therefore, an implication for the teaching of science includes designing curriculum materials and learning environments that create a social and epistemic context and invite students to engage in the practice of visualization as evidence, reasoning, experimental procedure, or a means of communication (as presented in the three case studies) and reflect on these practices (Ryu et al. 2015 ).

Finally, a question that arises from including visualization in science education, as well as from including scientific practices in science education is whether teachers themselves are prepared to include them as part of their teaching (Bybee 2014 ). Teacher preparation programs and teacher education have been critiqued, studied, and rethought since the time they emerged (Cochran-Smith 2004 ). Despite the years of history in teacher training and teacher education, the debate about initial teacher training and its content still pertains in our community and in policy circles (Cochran-Smith 2004 ; Conway et al. 2009 ). In the last decades, the debate has shifted from a behavioral view of learning and teaching to a learning problem—focusing on that way not only on teachers’ knowledge, skills, and beliefs but also on making the connection of the aforementioned with how and if pupils learn (Cochran-Smith 2004 ). The Science Education in Europe report recommended that “Good quality teachers, with up-to-date knowledge and skills, are the foundation of any system of formal science education” (Osborne and Dillon 2008 , p.9).

However, questions such as what should be the emphasis on pre-service and in-service science teacher training, especially with the new emphasis on scientific practices, still remain unanswered. As Bybee ( 2014 ) argues, starting from the new emphasis on scientific practices in the NGSS, we should consider teacher preparation programs “that would provide undergraduates opportunities to learn the science content and practices in contexts that would be aligned with their future work as teachers” (p.218). Therefore, engaging pre- and in-service teachers in visualization as a scientific practice should be one of the purposes of teacher preparation programs.

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Evagorou, M., Erduran, S. & Mäntylä, T. The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to ‘seeing’ how science works. IJ STEM Ed 2 , 11 (2015). https://doi.org/10.1186/s40594-015-0024-x

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Architecture and Masks: A Visual Representation of Time

• Written by Eduardo Souza e Romullo Baratto | Translated by Tarsila Duduch
• Published on October 29, 2020

The Avions Voisin C7 was manufactured between 1924 and 1928 and featured a groundbreaking design for the time. The extensive use of glass, aluminum bodywork, and sharp angles hinted at the shapes of an aircraft. This was the car that Le Corbusier liked to park in front of his buildings - the architect considered this car to be the ultimate translation of modern age and technology combined into a single object. He was convinced that architecture had much to learn from this machine.

With 3 gears and a 30-horsepower engine, it is hard to imagine anyone using this car today since the automobile industry has experienced countless innovations since that time. Corbusier's architecture, however, doesn't seem so outdated, but the cars pictured alongside the brand new buildings are actually what reveals how old the photograph is. Locating elements that can point out the time period of a photograph is very effective, especially in architecture. Some elements can make this task much easier, for example, household appliances, computer monitors, or other particular details.

The subject of documenting the period of time in which an architectural image was created has been on the table for a long time now. However, it is not only about what the photographs show, but how they show it. Ignasi de Solà Morales, in his book Territorios (2002) , offers an interesting insight into the relationship between the city and its images. According to him, the way of representing a city - and, therefore, its architectures - changes as technology develops and, more importantly, when the current media aesthetics no longer respond to the complexities of the city and urban life.

Paris, London, and Vienna, which the author refers to as capital cities , have shown intense urban and industrial development since the mid-19th century. Not surprisingly, the most common form of visual representation of the world up to then - paintings - was gradually replaced by photography, a technology from that era that becomes a tool for capturing the great transformations that took place in those urban centers. "Images of accumulation, of agglomeration, of the masses on the streets," marked the period. The author goes on to say that "the capital city presents itself in these ways. Distant perspectives focusing on a public monument, a railway station, or an opera theater replace the picturesque aspect of the 18th-century veduta. "[1]

Cities like Barcelona and Berlin experienced rapid growth and structural changes in the first decades of the 20th century, together with a shift in the forms of representation of urban life. Photographs that focused on monuments or architectural masterpieces no longer managed to portray these cities that were fragmented throughout the territory so the pictures started to be replaced by photomontages and collages that didn't focus on a specific subject but instead revealed a blurred, uncentralized view of the cities. Collages by Paul Citroën, László Moholy-Nagy, and El Lissitzky, just to name a few, contribute to an outlook that not only portrays the city's growth but also the social and cultural atmosphere of that historical moment.

These transformations in the styles of representation of the city, as a result of the particularities of the current times, took place throughout the entire 20th century and can also be observed in architectural images. The photographs of the Case Study Houses by Julius Shulman are a fine example. The project was sponsored by Arts & Architecture magazine between 1945 and 1966 and had internationally renowned architects to design functional and affordable single-family residences that would embody the spirit of that time - a period marked by the end of World War II and the intense growth and sprawl of American cities. Some of these projects became emblematic works of the so-called International Style.

These houses represented more than just architecture, but a lifestyle - a dream -, and the images should express this desire. Shulman's photographs of Houses #9, #20B, #21B, and #22 , for example, are not merely a documentation of architectural materials - walls, floor, ceiling -, they reflect a modern space that is experienced and occupied by its inhabitants, who are also modern. Furniture, clothing, and sometimes even colors can transport us to a time around the 1940s and 1960s when smoking was not bad for your health and the dream of every American citizen was to have their own house in the suburbs with a swimming pool and a car - far more modern than Le Corbusier's Avions Voisin C7 - in the garage. Nowadays, the images of Shulman are outdated. However, his ability to represent not only space but also time is undeniable.

Another way of capturing time can be seen in the extreme long-exposure photographs of Michael Wesely. The German artist created cameras that allow the same negative film to be exposed for long periods: hours, days, or even years. The result is an overlapping image with many layers, sharp and blurred, that somehow depicts everything that went through the frame while the shutter was open. His most famous series is probably the reconstruction of Potsdamer-Leipziger Platz in Berlin between 1997 and 1999, which resulted in images that blend the city landscape in the background, scaffolds, and smudges of new buildings. Guilherme Wisnik states that "the disruption of the sharpness in the scenes casts light on the fact that every synthetic image is an illusion since they always carry countless hidden aspects that we cannot grasp, or that we prefer not to see." [2] 

Many aspects of the present-day, i.e. the times we live in, have been increasingly incorporated into the images of a space, either through an aesthetic approach - the collages of Moholy-Nagy and the extreme long-exposure photographs of Wesely for instance - or through elements and objects displayed in the picture - remember Le Corbusier's car and Shulman's photographs. Most recently, in the course of 2020, many projects submitted for publication in ArchDaily have shown people wearing masks. These people are more than just human-scale figures, they are actors in these images, witnesses of the historical period in which these works were photographed. Whether in residential environments such as the RM House by Pedro Miguel Santos , commercial spaces like the Apple Marina Bay Sands by Foster + Partners , or urban areas such as a renovation project in Shenzhen , this decision to incorporate them into the architectural image is proof of how the virus has changed everyday life, changed how we live and how we relate to spaces.

Masked, 1.5m away from each other, these human figures have unveiled the astonishing truth that we are not living an exception: coronavirus is already our history; coronalife [3], it’s our present. 

• [1] Solà-Morales, Ignasi de. Territorios . Barcelona: Editorial Gustavo Gili, 2002 / 62 pp.
• [2] Wisnik, Guilherme. Dentro do Nevoeiro: arquitetura, arte e tecnologia contemporânea . São Paulo: Ubu Editora, 2018 / 352 pp.
• [3] Beiguelman, Giselle. Coronavida: pandemia, cidade e cultura urbana . São Paulo: ECidade, 2020.

This article is part of the ArchDaily Topic: Human Scale . Every month we explore a topic in-depth through articles, interviews, news, and projects. Learn more about our monthly topics here . As always, at ArchDaily we welcome the contributions of our readers; if you want to submit an article or project, contact us .

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Time is an exceptional dimension with high relevance in medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data via visual representations enables us to learn from the past in order to predict, plan, and build the future.

This second edition builds upon the great success of the first edition. It maintains a brief introduction to visualization and a review of historical time-oriented visual representations. At its core, the book develops a systematic view of the visualization of time-oriented data. Separate chapters discuss interaction techniques and computational methods for supporting the visual data analysis. Many examples and figures illustrate the introduced concepts and techniques.

• Computer-Generated Visual Representations
• Time-Oriented Data
• Human-computer Interaction
• Visualization
• Visual analytics

Table of contents (8 chapters)

Front matter, introduction.

• Wolfgang Aigner, Silvia Miksch, Heidrun Schumann, Christian Tominski

Historical Background

Time and time-oriented data, crafting visualizations of time-oriented data, involving the human via interaction, computational analysis support, guiding the selection of visualization techniques, back matter, authors and affiliations.

Wolfgang Aigner

Silvia Miksch

Institute for Visual & Analytic Computing, University of Rostock, Rostock, Germany

Heidrun Schumann, Christian Tominski

About the authors

Wolfgang Aigner is the scientific director at the Institute of Creative\Media/Technologies at the St. Pölten University of Applied Sciences, Austria and adjunct professor at TU Wien, Austria. In 2013, he received his habilitation in computer science for his work on "Interactive Visualization and Data Analysis: Visual Analytics with a Focus on Time" from TU Wien. From 2006 to 2010, he was deputy head of the Department of Information and Knowledge Engineering at Danube University Krems, Austria and from 2010 to 2013, he was deputy head of the Laura Bassi Centre of Expertise for Visual Analytics Science and Technology (CVAST) at TU Wien, Austria. Dr. Aigner is an expert in information visualization and visual analytics, particularly in the context of time-oriented data, where he has authored and co-authored more than 140 scientific publications. He served as program committee member and chair for various scientific conferences and acts as associate editor for scientific journals. He has received national awards for his research work, was awarded a Top Cited Article 2005–2010 from Pergamon/Elsevier, and received a best paper honorable mention at the IEEE Conference on Visual Analytics Science and Technology (VAST). Since 2002, Wolfgang has been involved in the acquisition and execution of a number of funded basic and applied research projects at the national and international levels.

Silvia Miksch is a university professor and head of the research division "Visual Analytics" (CVAST), Institute of Visual Computing and Human-Centered Technology, TU Wien since 2015. She has been head of the Information and Knowledge Engineering research group, Institute of Software Technology & Interactive Systems, TU Wien from 1998 to 2015. From 2006 to 2010 she was university professor and head of the Department of Information and Knowledge Engineering (ike) at Danube University Krems, Austria. In April 2010 she established the awarded Laura Bassi Centre of Expertise for Visual Analytics Science and Technology (CVAST) funded by the Federal Ministry of Economy, Family and Youth of the Republic of Austria. Silvia has acquired, led, and has been involved in several national and international applied and basic research projects. She served as paper co-chair of several conferences including IEEE VAST 2010, 2011, and 2020 and overall papers chair IEEE VIS 2021 as well as EuroVis 2012, and on the editorial board of several journals including IEEE TVCG and CGF. She acts on various strategic committees, such as the VAST and EuroVis steering committees as well as the VIS executive committee. In 2020 she was inducted into the IEEE Visualization Academy. Furthermore, she acts as scientific reporter in the board of the Austrian Research Fund (FwF) and is advisory board member of the Vienna Science and Technology Fund (WWTF). She has more than 300 scientific publications and her main research interests are visualization and visual analytics over time and space with particular focus on interaction techniques, network-based, knowledge-assisted, and guidance-enriched methods.

Heidrun Schumann is a professor emeritus at the University of Rostock, Germany, where she was heading the Chair of Computer Graphics at the Institute for Visual & Analytic Computing. She received doctoral degree (Dr.-Ing.) in 1981 and post-doctoral degree (Dr.-Ing. habil.) in 1989. Her research and teaching activities cover a variety of topics related to computer graphics, including information visualization, visual analytics, and rendering. She is interested in visualizing complex data in space and time, combining visualization and terrain rendering, and facilitating visual data analysis with progressive methods. A key focus of Heidrun's work is to intertwine visual, analytic, and interactive methods for making sense of data. Heidrun published more than two hundred articles in top venues and journals. She co-authored the first German textbook on data visualization in 2000 and a textbook specifically on the visualization of time-oriented data in 2011. In 2014, Heidrun was inducted as a Fellow of the Eurographics Association. In 2020, she was awarded with the Fraunhofer Medal. In the same year, she was inducted to the IEEE Visualization Academy.

Christian Tominski is a professor at the Institute for Visual & Analytic Computing at the University of Rostock, Germany. He received doctoral (Dr.-Ing.) and post-doctoral (Dr.-Ing. habil.) degrees in 2006 and 2015, respectively. In 2021, he was granted the title of professor (apl. Prof.) for human-data interaction. His main research interests are in visualization of and interaction with data. He is particularly interested in effective and efficient visual analytics techniques for interactively exploring, analyzing, and editing complex data. Christian has published numerous papers on new visualization and interaction techniques for multivariate data, temporal data, geo-spatial data, and graph data. In his work, he emphasizes conceptual aspects and aims to systematically investigate visual analytics challenges. He co-authored three books, including a book on the visualization of time-oriented data in 2011, a book focusing on interaction for visualization in 2015, and a more general book about interactive visual data analysis in 2020. Christian is the maintainer of several visual analytics systems and visualization tools, including the LandVis system for spatio-temporal health data, the VisAxes tool for time-oriented data, the CGV system for coordinated graph visualization, the Responsive Matrix Cells for exploring and editing multivariate graphs, and the TimeViz Browser for visualization techniques for time-oriented data.

Bibliographic Information

Book Title : Visualization of Time-Oriented Data

Authors : Wolfgang Aigner, Silvia Miksch, Heidrun Schumann, Christian Tominski

Series Title : Human–Computer Interaction Series

DOI : https://doi.org/10.1007/978-1-4471-7527-8

Publisher : Springer London

eBook Packages : Computer Science , Computer Science (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s) 2023

Hardcover ISBN : 978-1-4471-7526-1 Published: 22 December 2023

Softcover ISBN : 978-1-4471-7529-2 Published: 22 December 2023

eBook ISBN : 978-1-4471-7527-8 Published: 21 December 2023

Series ISSN : 1571-5035

Series E-ISSN : 2524-4477

Edition Number : 2

Number of Pages : XIX, 441

Number of Illustrations : 57 b/w illustrations, 250 illustrations in colour

Topics : User Interfaces and Human Computer Interaction , Visualization

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College of Arts and Science

Students ponder visual representations of time with jenann ismael.

Written by Arman Aboutorabi, CAS communications intern

Jenann Ismael

On November 21, professor of philosophy at Columbia University Jenann Ismael addressed a large crowd in Shriver Center to give a special lecture titled "Visual Representations of Time: From Physics to Philosophy." Its purpose was to examine the role of pictorial representations in our understanding of time.

Ismael's lecture was part of this year's John W. Altman Program on "Time and Temporality." The Altman Program is a yearlong, interdisciplinary study of a salient public issue sponsored by Miami's Humanities Center. The largest research program in the humanities at Miami, the Altman brings together a team of faculty, students, and distinguished visitors from a wide range of academic disciplines to study the year's special topic.

This year's topic has been popular with students and faculty. "When I first arrived, the entire room was packed," said Anna Skudlarek , a senior majoring in English Literature and Women's, Gender, and Sexuality Studies. "I had to stand in the back until somebody brought in more chairs."

Ismael began by outlining the two major philosophical understandings of time in our universe. The first claims that the universe is open-ended and ongoing, while the second sees the universe as a contained, discrete manifold of events.

"What I want to talk about is the role of the image of time in these controversies," said Ismael, shifting the focus of the lecture away from philosophy and more towards physics.

Ismael first extolled the virtues of pictorial representation, praising how a picture helps the human brain understand complex problems and associations.

"It takes an Isaac Newton to see the similarity between cannonball trajectory and planetary motion, but any high school freshman can see the similarity in a well-depicted graphic," she said.

However, Ismael made clear that the process of representing space-time pictorially is not without drawbacks. Sometimes it causes philosophical misunderstandings.

"Four-dimensional images of space-time work by creating a point of view outside of the universe, treating the universe as an observable object," she explained. The problem with this approach is that "there is no external dimension outside of space and time; there is just space and time itself."

This way of representing space-time has twisted our understanding of the universe and confused many brilliant thinkers, said Ismael. It invites us to believe that the future already exists within the universe, when in fact it does not.

Ismael offered an alternative way of understanding time: "We discover what time looks like, not from the perspective of a fictional creature outside of time, but from the perspective of a creature within time who, like ourselves, experiences time minute-by-minute, second-by-second."

Ismael likened this approach to the experience of listening to music. Although an entire musical piece can be written out on a score, we experience music a series of notes in time, but our memory of previous notes and expectation of future notes puts together the musical piece as a whole.

"What your mind does with the events of your life as they are encountered and in the intervals that separate them is essential to temporal experience," she said.

Ismael's thought-provoking lecture was received well by the audience of students and faculty. Junior English literature and French major Mady Neal marveled at the interdisciplinary nature of Ismael's work.

"It was really interesting to see philosophy and physics be put together in this unique way," she said. "Seeing different areas of study come together like this to explain the same topics makes me think about all the cool ways different types of research can connect to each other."

"One of the goals of the Altman Program is to challenge people to rethink seemingly obvious features of our world," said Timothy Melley , director of the Miami University Humanities Center. "Ismael revealed how easy it is for even brilliant people to make mistakes when they get too caught up in comfortable ways of thinking."

If interdisciplinary lectures such as Dr. Ismael's interest you, visit the Humanities Center website to see upcoming events and learn about how to participate in this unique program at Miami.

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• Published: 22 April 2024

Memorability shapes perceived time (and vice versa)

• Alex C. Ma   ORCID: orcid.org/0000-0001-5824-0806 1 ,
• Ayana D. Cameron 1 &
• Martin Wiener   ORCID: orcid.org/0000-0001-5963-5439 1  

Nature Human Behaviour volume  8 ,  pages 1296–1308 ( 2024 ) Cite this article

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• Human behaviour
• Pattern vision
• Sensory processing

Visual stimuli are known to vary in their perceived duration. Some visual stimuli are also known to linger for longer in memory. Yet, whether these two features of visual processing are linked is unknown. Despite early assumptions that time is an extracted or higher-order feature of perception, more recent work over the past two decades has demonstrated that timing may be instantiated within sensory modality circuits. A primary location for many of these studies is the visual system, where duration-sensitive responses have been demonstrated. Furthermore, visual stimulus features have been observed to shift perceived duration. These findings suggest that visual circuits mediate or construct perceived time. Here we present evidence across a series of experiments that perceived time is affected by the image properties of scene size, clutter and memorability. More specifically, we observe that scene size and memorability dilate time, whereas clutter contracts it. Furthermore, the durations of more memorable images are also perceived more precisely. Conversely, the longer the perceived duration of an image, the more memorable it is. To explain these findings, we applied a recurrent convolutional neural network model of the ventral visual system, in which images are progressively processed over time. We find that more memorable images are processed faster, and that this increase in processing speed predicts both the lengthening and the increased precision of perceived durations. These findings provide evidence for a link between image features, time perception and memory that can be further explored with models of visual processing.

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Primary visual cortex straightens natural video trajectories

Visual temporal attention from perception to computation

Noisy and hierarchical visual memory across timescales

Data availability.

All behavioural data for these experiments, as well as the rCNN results and memorability images used, are available at https://doi.org/10.17605/OSF.IO/FX3N2 (ref. 96 ).

Code availability

All relevant toolboxes and code repositories are cited in the text. The code is available at https://doi.org/10.17605/OSF.IO/FX3N2 (ref. 96 ).

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Mehrer, J., Spoerer, C. J., Jones, E. C., Kriegeskorte, N. & Kietzmann, T. C. An ecologically motivated image dataset for deep learning yields better models of human vision. Proc. Natl Acad. Sci. USA 118 , e2011417118 (2021).

Wiener, M. Data and code for ‘Memorability shapes perceived time (and vice versa)’. OSF https://doi.org/10.17605/OSF.IO/FX3N2 (2024).

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Acknowledgements

We thank A. Oliva, J. Ongchoco, T. Konkle and T. Kietzmann for their helpful comments relating to the stimuli, results and findings in this manuscript. The authors received no specific funding for this work.

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M.W., A.D.C. and A.C.M. conceived of and designed the experiments. A.D.C. and A.C.M. collected and analysed the data, with consultation and additional analyses by M.W. A.C.M. and M.W. wrote the manuscript.

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Ma, A.C., Cameron, A.D. & Wiener, M. Memorability shapes perceived time (and vice versa). Nat Hum Behav 8 , 1296–1308 (2024). https://doi.org/10.1038/s41562-024-01863-2

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Your perception of time is skewed by what you see.

• Lilly Tozer

Nature (2024)

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Featured Story

The representation of time in information design.

“As in the case of space, where it is believed that it appears only where matter occurs and these notions are inseparable, time (time passing, as a matter of fact) takes place only where events occur.” E. Hajnicz . “Time Structures”

About the age of three, children are already capable of understanding the temporal priority principle stating that causes must always precede their effects. This is a fundamental principle in causal reasoning, and its inviolability shapes our linear perception of time.

In information design, this perception usually translates into a horizontal axis with time running from left to right. A timeline, combined with some sort of graphic notation to mark a sequence of events, is the most common way to visually present temporal data.

Time as a framework

The use of time as a structuring device in information design can be traced back to some early historiographic works. From the fourth century, in Europe, most attempts to structure visually historical events took the unsurprising shape of a chronologic table. The most prominent of these early examples is Eusebius’ Chronicon. Eusebius had developed a sophisticated table structure  (Rosenberg and Grafton 2010, p.15) that worked as parallel timelines, comparing Jewish, pagan, and Christian chronologies.

Later on, during what is now known as the golden age of statistical graphics  (Friendly 2008), the visual representation of temporal data took more diverse forms.

“The timeline seems among the most inescapable metaphors we have. And yet, in its modern form, with a single axis and a regular, measured distribution of dates, it is a relatively recent invention. Understood in this strict sense, the timeline is not even 250 years old.” D. Rosenberg, A. Grafton . “Cartographies of Time: A Visual History of the Timeline”

In the mid-eighteenth century, new linear formats emerged and were quickly adopted. Notable examples from that period are Joseph Priestley ‘ s famous time charts. His 1765 Chart of Biography traces the life of about two thousand historical figures born between 1200 B.C. and 1800 A.D.

Priestley’s time map was organized into several thematic classes and used horizontal lines to convey duration. The date of birth and death of each person were marked by the beginning and the end of the line. Certainty was expressed by a full line, while uncertainty in the occurrence of an event was expressed by a dotted or a broken line (Priestley 1764, p.11).

A few years later, in 1769, followed The New Chart of History. The simplicity and visual language of Priestley’s first chart were further developed to provide, what he believed to be “the most excellent mechanical help to the knowledge of history” (Priestley 1786, p.11).

If a person carries his eye horizontally, he sees, in a very short time, all the revolutions that have taken place in any particular country, and under whose power it is at present; and this is done with more exactness, and in much less time, than it could have been done by reading. J. Priestley. “A Description of A New Chart of History”

This approach had many adepts. David Rumsey, for example, adopted the same technique for visualizing historical and biographical data but combined them in a single chart: The Historical and Biographical Chart of the United States.

Priestley’s famous timelines also influenced the work of William Playfair – a pioneer in statistical graphics who invented the time-series line graph, the bar chart, and the pie chart. In 1786, Playfair published his Commercial and Political Atlas , featuring 43 time series line graphs and presumably the first ever bar chart.

Playfair drew inspiration for the bar chart from Priestley’s work, but the roots of his time series line graphs can be traced back to his childhood. His elder brother – the world-renowned geologist and mathematician John Playfair – made him keep a graphical record of daily temperatures. This simple task taught William that “whatever could be expressed in numbers could be represented by lines” (Klein 1995) and became the inspiration for his time series charts.

Line graphs were developed by William Playfair largely to show the changes in economic indicators (national debt, imports, exports) over time and to show the differences and relations among multiple time-series. Playfair also invented comparative bar charts to show relations of discrete series for which no time metric was available (e.g., imports from and exports to England) and pie charts and circle diagrams to show part-whole relations. M. Friendly. “The Golden Age of Statistical Graphics”

The 19th century was even richer in graphical innovations. Some of the best works from that period visualize data in relation to time.

Florence Nightingale invented the polar area diagram to show the causes of mortality among British soldiers during the Crimean War ; Francis Galton’s methods of mapping the weather included small multiples to provide a snapshot of the conditions on each day during December 1861; while Minard used his famous flow map technique to visualize how Napoleon’s Russian campaign unfolded in space and time.

Meanwhile, in the USA, Francis A. Walker became the nation’s leading statistician and was appointed superintendent of both the 1870 and 1880 census. This led to the publication of the First U.S. Statistical Atlas in 1875 that was recognized worldwide as a work of excellence.

Following the next two censuses, the Atlas was updated under the supervision of chief geographer Henry Gannett, whose magnificent work pushed the boundaries of cartography and statistical graphics even further. These new national atlases showed changes in statistical data over time through a wide range of visualization techniques.

Real-time data

By the end of the 19th century, real-time data recording methods also surged. The work of Eadweard Muybridge and especially Étienne-Jules Marey is noteworthy. They both developed new methods for visualizing time and motion by means of chronophotography.

Marey understood movement as a function of space and time. He was interested in the application of new graphical methods both as a means of expression and as a research tool and wrote a book on that matter – “La Méthode graphique dans les sciences expérimentales”.

His passion for collecting real-time data – whether it involved the study of motion or experimental physiology – inspired him to develop various recording devices. The sphygmograph, for example, recorded the pulse graphically in real-time. He also invented the “chronophotographic gun” which is considered a focal point in the history of cinematography.

For Marey, these new devices all represented a development of the chronographic tradition of Priestley and Playfair, and he was explicit about the connection. But while Priestley and Playfair created systems for representing temporal phenomena, Marey’s interest was in graphically recording them. D. Rosenberg and A. Grafton. “Cartographies of Time: A Visual History of the Timeline”

Beyond the linear metaphor

Time puts structure in place. Observing changes over time allows us to make assumptions about the future or to identify trends and cyclical patterns in the data. There are numerous methods for visualizing changes over time including bar charts; line graphs; Gantt charts; heat maps; stacked graphs; small multiples etc.

More sophisticated data visualization methods include, but are not limited to horizon charts and cycle plots. We will discuss all of these as well as other concepts related to time, such as temporal primitives and time structures in part two of the blog post. We will also discuss how location and time intersect in urban data analysis.

Image sources:

1.John Cage, Experimental music notations. The image comes from this amazing gallery . 2. Joseph Priestley, Specimen of a New Chart of Biography (1765). Image source: Joseph Priestley House . 3. Joseph Priestley, Detail from A New Chart of History (1769). Full-size image can be seen here . 4. David Rumsey, Historical and Biographical Chart of the United States (1810). Image source: “ Historical Charts and David Ramsay’s Narrative of Progress “. 5. William Playfair , Linear Chronology (1824). Source:  Wikipedia 6.William Playfair, Chart of Universal Commercial History (1805). From “ Commemorating William Playfair’s 250th birthday ”  7. Francis Galton, Small multiple diagrams visualizing weather conditions in December 1861. Source: Harvard University – Collection Development Department, Widener Library, HCL / Galton, Francis. Meteorographica 8. Francis A. Walker, Fiscal chart of the United States showing the course of the public debt by years from 1789 to 1870. Image courtesy:  David Rumsey map collection 9. Henry Gannett, United States exports. Detail from Scribner’s statistical atlas of the United States  (1883).  10. John Edwin Dingle, Aspects of the Course of China’s Trade for Half a Century (1917). Detail from The New Atlas and Commercial Gazetteer of China. Image courtesy: David Rumsey map collection . 11. John Edwin Dingle,  Aspects of China’s Trade in Gold and Silver Values (1917). Detail from The New Atlas and Commercial Gazetteer of China. Image courtesy: David Rumsey map collection . 12. Marey, E-J. – Heart rate data traced from a horse. La circulation du sang  (1881). Image source .

1. Friendly, M. (2008). “ The Golden Age of Statistical Graphics “. Statistical Science 2008, Vol. 23, No. 4, 502–535 2. Galton, F. (1863). Meteorographica, or Methods of Mapping the Weather . Macmillan, London. 3. Hajnicz, E. (1996). Time Structures. Formal Description and Algorithmic Representation . Lecture Notes in Artificial Intelligence, vol. 1047. Springer, Berlin, Heidelberg 4. Klein, J. (1995). The Method of Diagrams and the Black Arts of Inductive Economics , in: Rima, I. H. (ed.) Measurement, Quantification and Economic Analysis. Routledge, London & New York, p.116 5. Priestley, J. (1764). A Description of Chart of Biography. Warrington 6. Priestley, J. (1786). A Description of A New Chart of History: Containing a View of the Principal Revolutions of Empire that Have Taken Place in the World . J. Johnson, London. 7. Rosenberg, D. and Grafton A. (2010). Cartographies of Time: A Visual History of the Timeline. Princeton Architectural Press, New York, NY. 8. Symanzik, J., Fischetti, W., Spence, I. (2009). Commemorating William Playfair’s 250th Birthday . Computational Statistics 24 : 551–566.

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• Corpus ID: 62662903

The visual representation of time in timelines, graphs, and charts

• M. Mitchell
• Published 2004

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The line of time : backgrounds and usage of event-timelines, representation of time in digital calendars: an argument for a unified, continuous and multi-granular calendar view, more to reading images: motivations underlying horizontal and vertical time-related graphics, cross-cultural representations of musical shape, processing and analyzing assessment test logs provided by digital pen and paper, exploring community-based health care using mobile applications, 14 references, chapter 1 contributions of perceptual and cognitive processes to the comprehension of graphics, metaphors we live by, reading images: the grammar of visual design, animals in motion, the story of time, the society of mind, mechanical universe: the astrarium of giovanni de' dondi, the discovery of time, envisioning information, related papers.

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The 30 Best Data Visualizations of 2024 [Examples]

Written by: Anna Glivinska

Data is beautiful; it can inspire, improve lives and bring out the best in people. To keep you inspired, we’ve gathered the best data visualizations of 2024.

The chosen works cover a variety of topics from NASA asteroids in space to environmental issue statistics and futuristic LIDAR data graphs.

With over 4.54 billion people using the Internet in 2020, we’re sure to witness even more amazing data visualizations every year. For now, get ready to dive into 2024’s best data visualization examples. Enjoy your flight of imagination!

• NASA's Eyes on Asteroids is a good data visualization example that provides a great user experience. The design is simple and intuitive, making it easy for users to navigate the site and find what they're looking for.
• The History of Pandemics is an infographic that presents a visual timeline of every known pandemic and includes information on how many people were affected, where it spread and what caused it.
• Void of the Memories is the rarest data visualization on this list. It's a great combination of calligraphy and data visualization that tells the story of human memory and experience.
• The search for dark matter is one of the most important scientific questions in physics today, and this infographic, “The Search for Dark Matter,” serves as a great introduction to the subject.
• Enhance your data storytelling skills and creatively showcase your data by signing up for Visme's data visualization tools .

1 Nasa’s Eyes on Asteroids

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If you are interested in exploring data visualization topics in space exploration, check out this striking data visualization created by NASA.

NASA's Eyes on Asteroids is one of the best data visualizations due to its exceptional design and functionality. This interactive visualization allows users to explore the asteroid belt and see the real-time positions of asteroids in our solar system.

The design of this visualization is highly engaging and visually stunning, with a sleek and modern interface that is easy to use. The visualization features a 3D solar system model, allowing users to zoom in and out to explore asteroids and other celestial bodies.

One of the key features of NASA's Eyes on Asteroids visualization is its real-time data feed, which provides up-to-date information on the positions and trajectories of asteroids. This feature makes the visualization highly informative and relevant to current events, allowing users to track potentially hazardous asteroids and see their projected paths over time.

Design your own space exploration infographic using Visme. Allowing you to create data visualizations easier and faster.

Get inspired by one of our loyal Visme users, MacKenzie Stonis , Economic Research Analyst at Greater Memphis Chamber, who said:

"I have enough complications in life; I don’t need my report-building tool to add any fuel to the fire,” she laughs.  “I personally had experience with similar applications before Visme and found their tools weren’t as user-friendly as Visme, and their tools didn’t handle data very well. They didn’t provide the solution I really wanted."

2 Selfiecity – The Science of Selfies

Selfiecity is an innovative and engaging data visualization project exploring the selfies world. It uses a variety of visualizations to analyze selfies from five cities around the world.

They collected over 120,000 selfies from the five cities and selected nearly 1,000 photos from each town. After collecting the images, they analyzed various metrics such as demographics, poses, moods and features.

The project then revealed exciting insights into the culture and social behavior of the people who take selfies. For example, the project shows that women take more selfies than men and that people tend to take selfies in public places rather than private spaces.

The study was quite complex and yielded valuable insights, which presented a challenge when it came to sharing the results . However, the team did an excellent job creating visually appealing data visualizations to present the information.

3 The Ancient Seven Wonders of the World

The civil engineering feats of humankind have reached the highest peaks of the mountains and deep into the ocean, and we have built pyramids, temples and statues that are still standing today.

The seven wonders of the ancient world are a collection of man-made structures that are considered to be remarkable feats at the time they were built.

Pranav Gavali, a Data Scientist, created this graphic using data from Encyclopedia Britannica and Wikipedia to visualize the world's seven ancient wonders along with their features and modern-day locations.

The graphic perfectly illustrates how the seven wonders were built and why they are considered a wonder of the world. The Great Pyramid of Giza is the only one of the seven wonders that still stands today.

Design an infographic like this one using Visme’s pre-designed content blocks and infographic templates . Include live data visualizations by connecting to your Google or Excel spreadsheets. When connecting your Visme charts to Excel Online, select full sheets or only a specific range. Plus, when values change in your linked sheet, the chart is This is a prime example of how creative design can bring data to life

4 The World’s Population at 8 Billion

On November 15, 2022, the world’s population reached 8 billion. This is the first time in history that there have been this many people on Earth. And there can't be a more straightforward and visually appealing way to present this data than this visualization.

What makes this big data visualization stand out is its simplicity and effectiveness in conveying the message. Using a circle to represent the earth is a powerful symbol that makes the visualization easy to understand and remember.

By using colors to represent continents and lines to separate countries, the visualization effectively conveys the complexity of the world's population in a simple and visually appealing way.

5 The Top 10 Largest Nuclear Explosions

This is a prime example of how creative design can bring data to life. Beyond the interesting data visualization, it uses a unique approach, similar to an infographic, to showcase the impact and size of the largest nuclear explosions ever detonated.

It features a series of explosion image examples that help visualize each explosion's scale and impact. The use of images effectively conveys the destructive power of each blast in a way that is easy to understand and remember.

The data is presented clearly and concisely, with each explosion listed along with its country of origin.

6 Visualizing the History of Pandemics

This is an informative graphic named Visualizing the History of Pandemics by Nicholas LePan. It tells the story of all the known pandemics in the history of mankind, including the name of the disease, death toll and the approximate date the pandemic occurred.

While the exact number of victims of every disease is still under question, we can still learn from this graphic that super-spreading infections happened across all history of mankind. Statistical data of this infographic shows some diseases scaling with the growth of the population.

Striking 3D illustrations of diseases are combined with the research data from CDC, WHO, BBC, Wikipedia, Historical records, Encyclopedia Britannica and John Hopkins University. The illustrations scale according to the recorded death toll to allow scanning and recognizing data easily. 

7 It Fell From the Sky

Created by a UK-based designer, this infographic highlights beautiful data visualization of 34,000 meteorites that have fallen on the Earth. You will discover the map and timeline of the impacts per year, wrapped up in clean, stylish graphics. The visualization also shows spikes on the records and comparing the size of the biggest meteorites recorded. 

Meteorites hit almost all of Earth’s surface, but some areas seem untouched; this phenomenon could be connected with Earth’s magnetic fields. And who knows –  the future may bring us even more meteorites to explore! 

If you’re a fan of space and astronomy, you can learn more about meteorites from NASA website or check out this database of the Meteoritical Society.

Try Visme, our all-in-one design for creating stunning visualizations on meteorites in space or other research topics you’re working on.

Get the most out of Visme’s seamless integration with Google Sheets to create visualizations of live, easy-to-update data.

Link to your Google Sheets account or import through a link. Select the page and data range and connect them to your Visme chart. When the data changes in the Google Sheet, it automatically applies to the live project. Simply press the refresh button.

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8 Mars Mission 2024 Promo Reel

Vivid, rich in details. This 3D graphic uses beautiful data visualizations to share the vision of the future. Space missions and sending people into space are shown in an eye-catching red-grey palette.

The complicated animation of terrain exploration, space module flight and surface graphics are breathtaking. For a moment, you feel like a Mars mission crew member with your eyes on the stars.

9 Void of the Memories

These mesmerizing circles were brought to you by one of the best-in-class street art and calligraphy authors, Pokras Lampas. Whether you would like to decipher this canvas or refer to it as a pure visual object, the unique gothic Calligrafuturism style is an eye magnet for anyone.

The project is focused on the human consciousness and the theme of dreams in the context of human memory and experience. According to the author, the future is for global unity and harmony of cultures – and it’s visible in the fusion of styles, techniques and systems used in the project graphics. 

10 Plastic Waste Pollution 

Based on data on the distribution of total plastic waste generation by continent, Jamie Kettle created this personal project to estimate the percentage of plastic waste that was inadequately disposed of. 

The infographic provides a clear and precise picture of current surface plastic mass by ocean, measuring it in a creative way. We can see plastic waste management for every country in a colored bar chart. The names of the countries that report 100% of all their plastic waste handled properly are highlighted in bold. 

One of the major findings here is that the country's GDP and efficient plastic waste management aren’t always correlated—you can see this by the irregular patterns shown in the infographic.

If you are curious about plastic waste, here are some resources for you: a guide on plastic waste, detailed info on plastic waste pollution from the UN Environment Program and Impacts of Mismanaged Trash by the United States Environmental Protection Agency.

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11 Fossil Fuels

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While CO 2 buildup is responsible for climate change, the trend is projected to continue, and the infographic provides insight into when this could happen. It’s easy to notice a steady increase in fossil fuel emissions since the Industrial Revolution and the projected sharp rise in the concentration of carbon dioxide until 2100.

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12 Price of a Pandemic: Poverty Spreads Around the Globe

In this classic data visualization by National Geographic, data is placed against the dark background for better contrast and readability. Simple, comprehensive charts show us the effect of the pandemic on the income of people in various countries.

The authors distributed three levels of income range for countries with low and middle class income to provide a clear picture of the current situation. Core findings of the report were that the pandemic pushed a tremendous amount of people to extreme poverty – projected data is 100 million of people living on $1.90 per person/day.

Based on the World Bank data, the infographic provides a wide view of the exact factors influencing people’s wellbeing – from travel restrictions and job loss to wars, displacements and higher food costs. Highlights at the beginning reveal rapid shrinking of income in examined countries across all continents on a mass scale.

13 Water Consumption 

Hidden food production costs involve a great amount of freshwater. This stunning example of visualization created by Chesca Kirkland unfolds a story of water consumption required to produce certain kinds of food. 

From chocolate to cheese, coffee and beer, every product requires a certain amount of freshwater to grow or be produced. The second part of the infographic is centered on the water resources available, including the map of the water footprint per capita per year and general availability of clean water to people. 

Nominated for two C-Change Environmental and Sustainability Awards, the project won First Class Honours in Final Design Futures. Raising awareness about water sustainability is vital as we move forward to a more intelligent, AI-driven future.

We at Visme are inviting you to take up the challenge and create informative infographics that can invite change to various industry branches. Use our amazing free infographic library to create graphics for your personal projects as well as corporate or brand presentations. 

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14 Icebergs and Climate Change

Dedicated to “travel adventures” of this 4,200-square-kilometer iceberg, this infographic alerts people to climate change. A giant chunk of ice the length of Puerto Rico broke off the Antarctic peninsula coast to wander into the wild – and dangerously close to South Georgia Island, packed with wildlife.

The graphic compares the size of the berg with 66 countries or territories and cites that the ice mass is so large that it cannot be captured in one photograph. Besides, we can also see impressive geodata on the wildlife from the IUCN Red List of Threatened Species inhabiting the endangered South Georgia Island.

15  Cell Towers Map of the World

This stunning, elegant and creative visualization of 40 million cell towers is surely an unforgettable view. Based on OpenCelliD, the world's largest open database of cell towers, this interactive map is so far one of the most precise publicly available data sources for telecom-related projects.

We can see how the cell tower network lights up Europe and other big cities of the world; simultaneously, vast areas of “wilderness” are still present on the map. Harsh climate and low population density in the northern regions of Russia and Canada, along with central areas of Africa and Mongolia result in low quantity of cell towers in these areas.

Closeup view of this cell tower map resembles the brain structure. Similar to the neurons, axons and dendrites that create the communication network of the human body – cell towers keep humanity connected.

16  Active Satellites in Space

Created for Scientific American, this colorful and bright data visualization displays satellites in an original way. Neat and stylish satellite cluster grids sort them by country, orbit and class – business/commercial, civil, amateur/academic or defense.

The graphic details the mass of the satellites (100 kgs - 5,000 kgs), category (Test and Training, Communications, Images, Surveillance and Meteorology, Navigation and Research) and the launch date, from Nov 1974 till Aug 2020.

According to the graphic, six countries of the world control the largest amount of the satellites in orbit, and the US owns the largest share so far.

17  Covid Vaccination Tracker

Updated until July 15, 2022, this animated Covid vaccination tracker shows the percentage of people in the world given at least one dose. The infographic and data illustration displays data on the vaccination rollout plan in over 80 countries and 50 US states.

Data presented in this data visualization is sourced from the Our World in Data project at the University of Oxford. Uncluttered, simple graphs show the 7-day Covid vaccination rolling average as well. The interactive charts allow you to sort the percent of population given at least one dose by country or income.

At the bottom of the page we can see the detailed, in-depth Covid-19 vaccination statistics, with type of vaccines offered (Pfizer-BioNTech, Moderna, Sinopharm, CanSino, Oxford-AstraZeneca, Johnson & Johnson, Covishield, Sputnik V, etc.) and vaccination priority groups for various countries separately.

If you’re working on an infographic that includes map data, like this example, try Visme’s map data visualization tool . It comes equipped with a handy hover tooltip that labels country names and square footage. If you don’t need to show this data, you can hide it in the Map settings.

Create demographic visualization easily with Visme’s map templates . If you need to edit your map infographic on the go, you can do so from the mobile app on Android and iOS.

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It took 4 years to create this non-commercial self-funded project. Based on the eponymous sci-fi novel by Peter Watts, this visualization row includes breathtaking renders of the solar system, four-dimensional objects as a system of data visualization and manipulation, spacesuit interface renders, cryo capsule graphics and nonhuman species concepts.

The visualization received over a dozen awards and nominations such as Best VFX Screen Power Film Festival 2020, Outstanding Achievement Award (Sci-fi Short) Indie Short Fest LA 2020, Winner Best Sound & Music Fantasy/Sci-fi film Festival 2021, Award Winner Flickfair 2020, Official selection Miami International Sci-fi Film Festival 2021 and so on.

Space mysteries have always tempted mankind. With the outstanding talent of the team behind the project, we hope to enjoy the related movie one day.

19  Gravitational Waves

Introducing to you another captivating space-themed project – the interactive visualization of gravitational wave events. Created for Science News, this space-time ripples design is amazingly minimalistic, slick and informative.

This enchanting spiral animation is saturated with useful data about black hole mergers or cosmic smashups. You can learn about the original and final mass of the mergers, total merger size and other details of gravitational wave events. 

20  Map of the Lighthouses of Ireland

Updated my map of the lighthouses of Ireland from the #30DayMapChallenge - now with the correct timings/flash patterns etc. Thanks to @IrishLights for providing additional information pic.twitter.com/eLlicP8fw5 — Neil Southall (@neilcfd1) December 8, 2020

This great animation was created as a part of 30 Day Map Challenge and it depicts all lighthouses in Ireland according to their timing and flash patterns. Here, the author visualizes data from the IrishLights – the maritime organization delivering the safety service around the coast of Ireland. 

Aside from being a vital part of the water safety of coastal waterways, lighthouses are a symbol of hope and undying light even through the toughest circumstances. That’s one of the reasons why this minimalistic graphic is so appealing.

21  Together [Hierarchical Positions of Employees in a Corporation]

Good data visualizations are essential for conveying complex information in an easily understandable way. Look at this creative way of displaying the hierarchical organization structure in a large corporation with a presence in over 100 countries. This creative data visualization example looks fun and a bit otherworldly, with muffled but contrasting colors.

Linking C-level executives to their subordinates in every branch revealed an intricate and complex corporation structure. It’s suggested that in most cases, flat patterns would fail to represent company structures correctly because of the flexibility of human relations.

22  The Search for Dark Matter

The search for the ever elusive and intriguing dark matter continues. The problem isn’t likely to get solved any time soon – but here is a striking infographic for you to follow the lead.

Quanta Magazine created this interesting data visualization to represent the types of particles that dark matter could be made of. Axions, WIMPs, ultralight dark matter or primordial black holes – any of these could be a star candidate. 

Distributing every particle type along the scale according to their mass, the visualization also provides clear, concise descriptions for every type. Additionally, you can dive into the experiments’ data. Are you the one to solve the new puzzle in particle physics?

23  2020 Autonomous Vehicle Technology Report

Concise and lean, this comprehensive report draws focus to autonomous vehicle technology and provides an insight into the hardware & software market for self-driving vehicles. 

The report starts from the visualization explaining levels of autonomous vehicle capabilities in context of the environment. We learn that the greatest challenge for Google (Waymo), Uber and other companies building self-driving vehicles is to enable the vehicle to adjust to all driving scenarios.

Sensory technology is an essential part of autonomous vehicles, and they’re designed to build an environment map and localize themselves inside that map at the same time. This requires huge computational technologies – maps created by AI systems and humans are of great help here.

Further in the report, we see the visualization of the electromagnetic spectrum and its usage for perception sensors, graphics of the time-of-flight (ToF) principle of environment sensing and various object detection sensor types such as radars, cameras, LIDARs, MEMS, etc. The next visualization covers different sets of sensors used for autonomy by Tesla, Volvo-Uber and Waymo. 

Short, clean-cut schemes of the AI architecture of autonomous vehicles, the computation/decision making environment of an autonomous vehicle and the concept of vehicle-to-everything (V2X) communication complete the report.

24  The U.S. Election Twitter Network Graph Tool

These cutting edge visuals from the U.S. Election Twitter Network Graph Tool enables a viewer to analyze social media interactions that define the online political landscape. In this case, we’re tracking the influence and connections between various political figures.

It’s clearly visible which accounts the target account is most likely to mention or reply to. The network graphs clearly show the potential of certain accounts to generate new connections and influence their followers.

You can search for specific nodes in the interactive map. All information flow between nodes is reflected in the color of the node edges. Working together with other open-source investigation tools, this graph is meant to increase transparency and help fight misinformation in social networks.

25  Map of a Fly Brain

The high-resolution nervous system map represented in the above graphic is a part of the fruit-fly’s brain – yet the complexity and harmony of the structure is astounding. 

Millions of connections between 25,000 neurons create a wiring diagram, or connectome, of connections in various parts of a fruit fly’s brain.

It’s estimated that tracking all neuron connections in the fruit fly’s brain manually would need 250 people working for 20 years at least. Google’s computational power has helped to speed up this research, and scientists are aiming to create a full fruit fly brain visualization by 2022.

26  Freight Rail Works

Our next interesting visualization highlights the advanced layers of technology Freight Rail Works uses across its infrastructure. Talented Danil Krivoruchko & Aggressive/Loop teams produced a futuristic and dynamic animation of the data-world around a train in motion.

Magnificent waves of data light up outlines of the objects and then vanish in waves as the train moves forward to the smart city. Graphics of the giant city cluster zoom out to reveal the continent routes and the beauty of a simple railway communications network. 

In the era of semi-autonomous aircrafts and drones, the simple, down-to-earth railway system looks stable but innovative in this graphic.

27  The Korean Clusters

Korean hospitals and churches experienced a burst of Covid infections among their visitors in January 2020. Having linked connections between the confirmed cases, scientists were able to trace back the first case and build a tree of contacts between the affected people.

Tracking the timeline of the first patient’s actions revealed that this person caused thousands of infections. Wandering sick for a few days resulted in over 30 more people infected. Subsequently, the Shincheonji Church cluster with 5,016 infected people accounted for at least 60% of all cases in South Korea at that time.

28  2020’s Biggest Tech Mergers and Acquisitions

Despite the fact that for most businesses 2020 was a devastating year with grim outcomes, this data visualization shows that Big Tech experienced a growth boost. It’s not surprising that people working remotely increasingly need digital services of all kinds.

The graphic shows the biggest tech mergers and acquisitions closed in 2020, together with the short description of the acquired company, acquiring company, deal amount and deal date. While the chart is visually busy, it’s also innovative and visually appealing.

If you need a market report from your industry area, grab the data from Crunchbase and build your own custom branded infographic via our data visualization tool quickly and easily. Sign up free .

29  Stolen Paintings

This wonderful visualization was created for Visual Data, a column on "La Lettura," the cultural supplement of "Corriere Della Sera."

From 1900 to the present day, the infographic reveals the details of 40 stolen paintings. Neutral, minimalistic visuals highlight the painting’s artist, the year when the painting was created and the year of theft. 

It was shocking to find out that the majority of thefts took place during the last 20 years (2000-2020) – and most of the art works have never been recovered.

30  House Of Cards LIDAR

House of Cards from Brendan Dawes on Vimeo .

Take a look at the last cool data visualization in this list – the rework of Radiohead's House of Cards video. This astonishing art was created on the basis of around one minute of the LIDAR data.

Motion graphics of particles scattered around a person’s face create an unforgettable image. The hero of the story in the video is clearly emotional – but we can’t tell anymore whether this person is even human. 

AI generated data can be beautiful, but how can you take control?

Data Visualization FAQs

What is the most popular form of data visualization.

Bar graphs, bar charts or column charts are the most popular type of data visualization.

Bar charts are best for comparing numerical values across categories using rectangles (or bars) of equal width and variable height. You can use bar graphs to compare items between different groups, measure changes over time and identify patterns or trends.

Other popular forms of data visualization include pie charts , line graphs , area charts , histograms , pivot tables, boxplots, scatter plots , radar charts and choropleth maps.

What Are the Benefits of Data Visualization?

Here’s how data visualization helps users to make the most of their data.

• Data visualizations make data clear, concise and easy to understand. Users can easily unlock key values from massive data sets, interpret them and draw conclusions.
• Visualization allows business users to identify relationships, patterns and trends between data, giving it greater meaning. You can easily uncover fresh insights and focus areas that require more attention.
• Creative data visualization is about creating compelling narratives through the use of graphics, diagrams and visual analytics. Visualizing data helps users tell better stories and convey messages in an engaging manner.
• Data visualization can significantly increase the pace of decision-making processes since it makes it simple for us to understand visual data. It’s no surprise, as The Wharton School of Business says that data visualization can cut down on meeting time by up to 24% .

Visualizing data helps quickly spot any errors so they can be removed. If you still doubt the importance of data visualization, this article about 50 data visualization statistics might change your thought process.

What are the Best Practices of Data Visualization?

Below are data visualization best practices to help you present data in an engaging and appealing way.

• Specify the audience and their unique needs. Your data visualization should be crafted to communicate, provide real value and meet the needs of the target audience.
• Define a Clear Purpose. Specify what questions you want your data visualizations to answer or the problems you want them to solve.
• Keep your data clean. Before visualizing your data, make sure to fix or remove incomplete, duplicate, incorrect, corrupted and incorrectly formatted data within your dataset.
• Use the right visuals. With so many charts available, identify the best type for presenting the particular data type you’re working on.
• Keep your data organized. At a glance, your audience should be able to view and digest information quickly.
• Use the right color combination.

Read our article to learn more about data visualization best practices.

Create Your Own Data Visualizations

If you are feeling inspired by these cool data visualizations, use our data visualization software to convert disparate data into clean, comprehensive visuals using the best data visualization techniques . You'll find an extensive library of customizable charts and graphs including bubble charts, bar graphs , line charts , scatter plots, and much more. 

Wondering if Visme's data visualization tools are right for you? Take a look at what one of our satisfied customers, Cassandra C. | Owner, has to say:

“I also appreciate the wide range of features, including charts, graphs, and other visuals that can be used to present data in a clear and concise way. Overall, I'm very happy with Visme and would highly recommend it to anyone looking for a fun, user-friendly tool to create visuals.”

To learn more about creating your own data visualizations, check out our detailed guide on data visualization types and the introduction to data viz on our blog.

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Visualizations That Really Work

• Scott Berinato

Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone—but that convenience can lead to charts that are merely adequate or even ineffective.

By answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? and Am I declaring something or exploring something? He leads readers through a simple process of identifying which of the four types of visualization they might use to achieve their goals most effectively: idea illustration, idea generation, visual discovery, or everyday dataviz.

This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations.

Know what message you’re trying to communicate before you get down in the weeds.

Idea in Brief

Knowledge workers need greater visual literacy than they used to, because so much data—and so many ideas—are now presented graphically. But few of us have been taught data-visualization skills.

Tools Are Fine…

Inexpensive tools allow anyone to perform simple tasks such as importing spreadsheet data into a bar chart. But that means it’s easy to create terrible charts. Visualization can be so much more: It’s an agile, powerful way to explore ideas and communicate information.

…But Strategy Is Key

Don’t jump straight to execution. Instead, first think about what you’re representing—ideas or data? Then consider your purpose: Do you want to inform, persuade, or explore? The answers will suggest what tools and resources you need.

Not long ago, the ability to create smart data visualizations, or dataviz, was a nice-to-have skill. For the most part, it benefited design- and data-minded managers who made a deliberate decision to invest in acquiring it. That’s changed. Now visual communication is a must-have skill for all managers, because more and more often, it’s the only way to make sense of the work they do.

• Scott Berinato is a senior editor at Harvard Business Review and the author of Good Charts Workbook: Tips Tools, and Exercises for Making Better Data Visualizations and Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations .

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Visualize Learning: Mastering Time Concepts for Neurodivergent Students

In this post learn about visual tools for teaching time concepts to neurodivergent students and how you can support them learn time management skills.

Are you looking for ways to teach time concepts to neurodivergent kids and teens effectively? Parents and educators play a crucial role in helping these individuals understand the passage of time. Neurodivergent individuals, such as those with autism or ADHD, may face challenges in grasping abstract time concepts, leading to difficulties in time management and following schedules.

Visual tools have been proven to be incredibly beneficial in aiding the understanding of time for neurodivergent kids and teens. By incorporating visual aids like time timers and schedules, parents and educators can provide a concrete representation of time, making it more tangible and easier to comprehend. These tools help create a visual structure that enhances comprehension and reduces anxiety related to time management.

Imagine the impact of using visual timers as a game-changer in teaching time concepts. Visual timers like the Time Timer can help neurodivergent individuals track schedules, tasks, and transitions more effectively. By offering a visual representation of time passing, these tools create a sense of predictability and structure, empowering neurodivergent individuals to manage their time more efficiently.

In this blog post, we will explore the significance of visual tools in teaching time concepts to neurodivergent kids and teens. We will delve into practical strategies and recommended visual aids that parents and educators can leverage to support the understanding of time in this unique audience. Stay tuned to discover how visual tools can make a pivotal difference in helping neurodivergent individuals navigate the concept of time with confidence and ease.

Affiliate Disclosure

*Please note that some of the links in this post are affiliate links, which means I may earn a small commission at no extra cost to you if you make a purchase through these links. I only recommend products or services that I have personally used or believe will add value to my readers. This helps support my blogging efforts and allows me to continue bringing you valuable content. Thank you for your support!

Understanding Time Concepts for Neurodivergent Kids and Teens

Time concepts can be particularly challenging for neurodivergent kids and teens due to differences in cognitive processing. Neurodivergent individuals may struggle with understanding abstract notions of time, leading to difficulties in managing schedules and routines effectively. It's essential for parents and educators to recognize these unique challenges and implement strategies that cater to their specific needs.

Benefits of Visual Tools

Using visual tools to teach time concepts to neurodivergent kids and teens offers a range of advantages. Research suggests that visual aids can significantly improve time management skills by providing a tangible representation of the passage of time. These tools also play a vital role in reducing anxiety levels, as they offer a clear and structured way to interpret schedules and transitions. By incorporating visual tools, neurodivergent individuals can enhance their understanding of time-related concepts and develop more efficient coping mechanisms in daily activities.

For instance, visual schedules have been proven to be effective in helping neurodivergent children follow routines and anticipate upcoming events. Visual schedules use images, symbols, or words to represent activities in sequence, making it easier for individuals to comprehend the flow of their day. Research shows that visual schedules can enhance independence and self-esteem in neurodivergent individuals by providing a visual roadmap for their daily tasks.

Popular Visual Timer Products

Popular visual timer products like the Time Timer have gained recognition for their effectiveness in supporting neurodivergent children in time management. The Time Timer uses a visual countdown system that represents the elapsing time through a disappearing colored disk, offering a clear visual representation of time passing. This visual cue helps neurodivergent individuals grasp the concept of time more tangibly, facilitating better time management skills and task completion.

Other visual timer products, such as digital countdown timers and interactive timer apps, provide customizable features to suit individual preferences and needs. These tools can be integrated into daily routines to signal transitions between activities, manage homework time, or facilitate leisure activities. By incorporating visual timer products into learning environments, parents and educators can empower neurodivergent kids and teens to navigate time-related challenges more effectively.

Incorporating visual tools and timer products not only enhances time comprehension but also fosters independence and confidence in neurodivergent individuals. By leveraging visual aids tailored to their unique learning styles, parents and educators can create supportive environments that promote successful time management and routine adherence for neurodivergent kids and teens.

Implementing Visual Tools in Teaching Time Concepts

Incorporating visual tools can significantly benefit neurodivergent kids and teens in understanding time concepts. Visual aids help create a structured environment that aids comprehension and reduces anxiety. Let's explore practical ways to implement visual tools effectively.

Creating Visual Schedules

Creating visual schedules using tools like Time Timer can be instrumental in helping neurodivergent individuals manage their daily activities and transitions. Visual schedules provide a clear timeline of tasks, reducing uncertainty and promoting independence. By customizing schedules with images or color-coding, children can better anticipate and navigate their routines.  A Practical Guide to Creating Visual Schedules  offers insights into optimizing visual schedules for enhanced effectiveness.

Using Visual Timers for Transitions

Visual timers serve as valuable aids in facilitating smooth transitions for neurodivergent individuals. By visually representing the passage of time, these timers offer a tangible way to understand temporal concepts. Setting up visual timers and incorporating them into daily routines can help individuals manage transitions between activities effectively. Explore more about the benefits of visual timers in transition management in this article on  Reducing Transition Trouble with Time Timer Visual Timers .

Incorporating Visual Tools in Learning Activities

Integrating visual tools into educational activities can make learning time concepts more engaging and comprehensible for neurodivergent kids and teens. By creating interactive and hands-on learning experiences, educators can cater to diverse learning styles and enhance understanding. Discover innovative ways to incorporate visual tools in learning activities by exploring  4 Tools to Help Educators Meet the Needs of Neurodiverse Students  for inspiration.

By implementing visual schedules, timers, and tools in teaching time concepts, parents and educators can empower neurodivergent kids and teens to navigate time effectively and with confidence.

9 Visual Tools to Teach Time Concepts for Neurodivergent Kids and Teens

Understanding time concepts can be challenging for neurodivergent children and teens. Visual aids and tools can make a significant difference in helping them grasp abstract notions like time, schedules, and routines. This list explores ten effective visual tools to support neurodivergent learners in developing a solid understanding of time-related concepts.

1. Analog Clocks

Analog clocks provide a tangible representation of time, making it easier for neurodivergent learners to visualize the movement of the hour and minute hands. Start with simple clocks and gradually introduce more complex designs as they progress.

One of the best ways to do this is to color the clock so that they can see different colors representing the various sections of time. Here is an example of a colored learning clock you can purchase.

Learning Clocks to Teach Time

Teachers Choice Writable Dry Erase Learning Clock is a great tool to help teach time concepts. You can also get this Teachers Choice Writable Dry Erase Colored Learning Clock that has colored sections for each time on the clock.

2. Digital Timers and Visual Timers

Digital timers offer a clear, numerical display of time, which can be helpful for those who struggle with analog clock reading. Timers can be used for various activities, reinforcing the concept of time passing and duration. There are many different options of visual timers to meet your needs on Amazon. You could get large visual timers, a small visual timer to keep on a desk, a wrist watch as a visual timer, or even some other ideas that offer more audio feedback as well.

3. Visual Schedules

Visual schedules are a powerful tool for neurodivergent individuals. They break down daily routines into a series of images or symbols, providing a clear visual representation of the sequence of events and activities.

4. Calendars

Calendars, both physical and digital, can help neurodivergent learners understand the concept of days, weeks, months, and years. Use colorful calendars with large, clear markings to highlight important dates and events.

5. Time Sequencing Strips

Time sequencing strips are visual aids that depict the order of events or activities. They can be customized to suit individual needs and can be particularly useful for teaching daily routines or multi-step tasks.

6. Timelines

Timelines provide a visual representation of events or historical periods, helping neurodivergent learners grasp the concept of chronological order and the passage of time.

7. Video Modeling

Video modeling can be an effective way to demonstrate time-related concepts and skills. Short, engaging videos can show step-by-step processes, such as setting an alarm clock or reading a timetable.

8. Time-Telling Apps and Software

Various apps and software programs offer interactive and engaging ways to learn about time. Many of these tools incorporate gamification elements, making the learning process more enjoyable and motivating.

9. Social Stories

Social stories are short, descriptive narratives that use visual aids to explain social situations or concepts. They can be an effective way to teach neurodivergent individuals about time-related social expectations, such as being punctual or following schedules.

Visuals Tools to Help Teach Time Concepts

Using visual tools to teach time concepts to neurodivergent kids and teens can make a significant impact on their learning journey. By incorporating visual aids and techniques, parents and educators can create a supportive and engaging environment for neurodivergent individuals to understand the abstract concept of time better. 

Importance of Visual Tools

Visual tools, such as visual timers like the  Time Timer for children with autism , play a crucial role in helping neurodivergent kids grasp the passing of time. These tools provide a tangible representation of time passing, making it easier for children to manage tasks and transitions effectively.

Encouraging Exploration

Parents and educators are encouraged to explore a variety of visual aids beyond timers, like colorful calendars, interactive clocks, and visual schedules. These resources can cater to different learning styles and preferences, supporting neurodivergent kids and teens in comprehending time concepts more comprehensively.

Supporting the Learning Journey

By integrating visual tools into daily routines and educational activities, caregivers can create a structured and organized environment that promotes time awareness and independence in neurodivergent individuals. Consistent exposure to visual aids can enhance understanding, reduce anxiety, and facilitate smoother transitions throughout the day.

Empowering Neurodivergent Individuals

Visual tools empower neurodivergent kids and teens to navigate time-related tasks with confidence and autonomy. Through visual support, individuals can develop essential time management skills, improve focus and self-regulation, and ultimately enhance their overall learning experience.

In conclusion, the utilization of visual tools is a valuable strategy for parents and educators seeking to enhance time concepts learning for neurodivergent kids and teens. By implementing these tools thoughtfully and creatively, caregivers can create a supportive environment that nurtures time awareness and fosters independence in neurodivergent individuals.

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What is a Timeline? Timeline for Project Management

A timeline provides a visual representation of events that helps you better understand history, a story, a process or any other form of an event sequence arranged in chronological order and displayed along a line (usually drawn left to right or top to bottom). It explains what happened during a certain period or to a particular person, starting with the earliest event and moving forward through time. Increasingly, timelines are illustrated in infographics combining text and graphic images for a better presentation.

A timeline is useful to document for any type of development, providing an easy-to-understand history and helping viewers to understand past and ongoing quickly.

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Timeline for Project Manager

In project management, knowing how to create a project timeline is one of the most essential skills a project manager, as a timeline are most useful for showing important milestones, deadlines and other significant dates and events over the lifecycle of the project. Building comprehensive, accurate timelines will help a manager get every project off on the right foot.

In the example below, the timeline shows important events labeled in chronological order on a linear time scale. They are used in project management to help teams and stakeholders understand what milestones the project needs to achieve and the delivery date for each milestone.

Timelines can also be displayed in Gantt charts style with project schedules.

Off-the-Shelf Timeline Infographics

Visual Paradigm provides simple timeline templates that can help you make some good highlights on a series of events that have taken place in a certain timeframe. Whether it’s the history of your company, a roadmap of your project or a schedule of your implementation plan, a timeline infographic is the best way to communicate your message through storytelling matter. Creating a timeline infographics from scratch can to time-consuming and overwhelming, just adopt one of our nice timeline templates that you can jumpstart your design straight-away!

Timeline Examples

The best way to understand timelines is to look at some examples of timelines. Click and edit on any of these off-the-shelf timelines templates for jumpstarting your design:

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Creating visual explanations improves learning

Eliza bobek.

1 University of Massachusetts Lowell, Lowell, MA USA

Barbara Tversky

2 Stanford University, Columbia University Teachers College, New York, NY USA

Associated Data

Many topics in science are notoriously difficult for students to learn. Mechanisms and processes outside student experience present particular challenges. While instruction typically involves visualizations, students usually explain in words. Because visual explanations can show parts and processes of complex systems directly, creating them should have benefits beyond creating verbal explanations. We compared learning from creating visual or verbal explanations for two STEM domains, a mechanical system (bicycle pump) and a chemical system (bonding). Both kinds of explanations were analyzed for content and learning assess by a post-test. For the mechanical system, creating a visual explanation increased understanding particularly for participants of low spatial ability. For the chemical system, creating both visual and verbal explanations improved learning without new teaching. Creating a visual explanation was superior and benefitted participants of both high and low spatial ability. Visual explanations often included crucial yet invisible features. The greater effectiveness of visual explanations appears attributable to the checks they provide for completeness and coherence as well as to their roles as platforms for inference. The benefits should generalize to other domains like the social sciences, history, and archeology where important information can be visualized. Together, the findings provide support for the use of learner-generated visual explanations as a powerful learning tool.

Electronic supplementary material

The online version of this article (doi:10.1186/s41235-016-0031-6) contains supplementary material, which is available to authorized users.

Significance

Uncovering cognitive principles for effective teaching and learning is a central application of cognitive psychology. Here we show: (1) creating explanations of STEM phenomena improves learning without additional teaching; and (2) creating visual explanations is superior to creating verbal ones. There are several notable differences between visual and verbal explanations; visual explanations map thought more directly than words and provide checks for completeness and coherence as well as a platform for inference, notably from structure to process. Extensions of the technique to other domains should be possible. Creating visual explanations is likely to enhance students’ spatial thinking skills, skills that are increasingly needed in the contemporary and future world.

Dynamic systems such as those in science and engineering, but also in history, politics, and other domains, are notoriously difficult to learn (e.g. Chi, DeLeeuw, Chiu, & Lavancher, 1994 ; Hmelo-Silver & Pfeffer, 2004 ; Johnstone, 1991 ; Perkins & Grotzer, 2005 ). Mechanisms, processes, and behavior of complex systems present particular challenges. Learners must master not only the individual components of the system or process (structure) but also the interactions and mechanisms (function), which may be complex and frequently invisible. If the phenomena are macroscopic, sub-microscopic, or abstract, there is an additional level of difficulty. Although the teaching of STEM phenomena typically relies on visualizations, such as pictures, graphs, and diagrams, learning is typically revealed in words, both spoken and written. Visualizations have many advantages over verbal explanations for teaching; can creating visual explanations promote learning?

Learning from visual representations in STEM

Given the inherent challenges in teaching and learning complex or invisible processes in science, educators have developed ways of representing these processes to enable and enhance student understanding. External visual representations, including diagrams, photographs, illustrations, flow charts, and graphs, are often used in science to both illustrate and explain concepts (e.g., Hegarty, Carpenter, & Just, 1990 ; Mayer, 1989 ). Visualizations can directly represent many structural and behavioral properties. They also help to draw inferences (Larkin & Simon, 1987 ), find routes in maps (Levine, 1982 ), spot trends in graphs (Kessell & Tversky, 2011 ; Zacks & Tversky, 1999 ), imagine traffic flow or seasonal changes in light from architectural sketches (e.g. Tversky & Suwa, 2009 ), and determine the consequences of movements of gears and pulleys in mechanical systems (e.g. Hegarty & Just, 1993 ; Hegarty, Kriz, & Cate, 2003 ). The use of visual elements such as arrows is another benefit to learning with visualizations. Arrows are widely produced and comprehended as representing a range of kinds of forces as well as changes over time (e.g. Heiser & Tversky, 2002 ; Tversky, Heiser, MacKenzie, Lozano, & Morrison, 2007 ). Visualizations are thus readily able to depict the parts and configurations of systems; presenting the same content via language may be more difficult. Although words can describe spatial properties, because the correspondences of meaning to language are purely symbolic, comprehension and construction of mental representations from descriptions is far more effortful and error prone (e.g. Glenberg & Langston, 1992 ; Hegarty & Just, 1993 ; Larkin & Simon, 1987 ; Mayer, 1989 ). Given the differences in how visual and verbal information is processed, how learners draw inferences and construct understanding in these two modes warrants further investigation.

Benefits of generating explanations

Learner-generated explanations of scientific phenomena may be an important learning strategy to consider beyond the utility of learning from a provided external visualization. Explanations convey information about concepts or processes with the goal of making clear and comprehensible an idea or set of ideas. Explanations may involve a variety of elements, such as the use of examples and analogies (Roscoe & Chi, 2007 ). When explaining something new, learners may have to think carefully about the relationships between elements in the process and prioritize the multitude of information available to them. Generating explanations may require learners to reorganize their mental models by allowing them to make and refine connections between and among elements and concepts. Explaining may also help learners metacognitively address their own knowledge gaps and misconceptions.

Many studies have shown that learning is enhanced when students are actively engaged in creative, generative activities (e.g. Chi, 2009 ; Hall, Bailey, & Tillman, 1997 ). Generative activities have been shown to benefit comprehension of domains involving invisible components, including electric circuits (Johnson & Mayer, 2010 ) and the chemistry of detergents (Schwamborn, Mayer, Thillmann, Leopold, & Leutner, 2010 ). Wittrock’s ( 1990 ) generative theory stresses the importance of learners actively constructing and developing relationships. Generative activities require learners to select information and choose how to integrate and represent the information in a unified way. When learners make connections between pieces of information, knowledge, and experience, by generating headings, summaries, pictures, and analogies, deeper understanding develops.

The information learners draw upon to construct their explanations is likely important. For example, Ainsworth and Loizou ( 2003 ) found that asking participants to self-explain with a diagram resulted in greater learning than self-explaining from text. How might learners explain with physical mechanisms or materials with multi-modal information?

Generating visual explanations

Learner-generated visualizations have been explored in several domains. Gobert and Clement ( 1999 ) investigated the effectiveness of student-generated diagrams versus student-generated summaries on understanding plate tectonics after reading an expository text. Students who generated diagrams scored significantly higher on a post-test measuring spatial and causal/dynamic content, even though the diagrams contained less domain-related information. Hall et al. ( 1997 ) showed that learners who generated their own illustrations from text performed equally as well as learners provided with text and illustrations. Both groups outperformed learners only provided with text. In a study concerning the law of conservation of energy, participants who generated drawings scored higher on a post-test than participants who wrote their own narrative of the process (Edens & Potter, 2003 ). In addition, the quality and number of concept units present in the drawing/science log correlated with performance on the post-test. Van Meter ( 2001 ) found that drawing while reading a text about Newton’s Laws was more effective than answering prompts in writing.

One aspect to explore is whether visual and verbal productions contain different types of information. Learning advantages for the generation of visualizations could be attributed to learners’ translating across modalities, from a verbal format into a visual format. Translating verbal information from the text into a visual explanation may promote deeper processing of the material and more complete and comprehensive mental models (Craik & Lockhart, 1972 ). Ainsworth and Iacovides ( 2005 ) addressed this issue by asking two groups of learners to self-explain while learning about the circulatory system of the human body. Learners given diagrams were asked to self-explain in writing and learners given text were asked to explain using a diagram. The results showed no overall differences in learning outcomes, however the learners provided text included significantly more information in their diagrams than the other group. Aleven and Koedinger ( 2002 ) argue that explanations are most helpful if they can integrate visual and verbal information. Translating across modalities may serve this purpose, although translating is not necessarily an easy task (Ainsworth, Bibby, & Wood, 2002 ).

It is important to remember that not all studies have found advantages to generating explanations. Wilkin ( 1997 ) found that directions to self-explain using a diagram hindered understanding in examples in physical motion when students were presented with text and instructed to draw a diagram. She argues that the diagrams encouraged learners to connect familiar but unrelated knowledge. In particular, “low benefit learners” in her study inappropriately used spatial adjacency and location to connect parts of diagrams, instead of the particular properties of those parts. Wilkin argues that these learners are novices and that experts may not make the same mistake since they have the skills to analyze features of a diagram according to their relevant properties. She also argues that the benefits of self-explaining are highest when the learning activity is constrained so that learners are limited in their possible interpretations. Other studies that have not found a learning advantage from generating drawings have in common an absence of support for the learner (Alesandrini, 1981 ; Leutner, Leopold, & Sumfleth, 2009 ). Another mediating factor may be the learner’s spatial ability.

The role of spatial ability

Spatial thinking involves objects, their size, location, shape, their relation to one another, and how and where they move through space. How then, might learners with different levels of spatial ability gain structural and functional understanding in science and how might this ability affect the utility of learner-generated visual explanations? Several lines of research have sought to explore the role of spatial ability in learning science. Kozhevnikov, Hegarty, and Mayer ( 2002 ) found that low spatial ability participants interpreted graphs as pictures, whereas high spatial ability participants were able to construct more schematic images and manipulate them spatially. Hegarty and Just ( 1993 ) found that the ability to mentally animate mechanical systems correlated with spatial ability, but not verbal ability. In their study, low spatial ability participants made more errors in movement verification tasks. Leutner et al. ( 2009 ) found no effect of spatial ability on the effectiveness of drawing compared to mentally imagining text content. Mayer and Sims ( 1994 ) found that spatial ability played a role in participants’ ability to integrate visual and verbal information presented in an animation. The authors argue that their results can be interpreted within the context of dual-coding theory. They suggest that low spatial ability participants must devote large amounts of cognitive effort into building a visual representation of the system. High spatial ability participants, on the other hand, are more able to allocate sufficient cognitive resources to building referential connections between visual and verbal information.

Benefits of testing

Although not presented that way, creating an explanation could be regarded as a form of testing. Considerable research has documented positive effects of testing on learning. Presumably taking a test requires retrieving and sometimes integrating the learned material and those processes can augment learning without additional teaching or study (e.g. Roediger & Karpicke, 2006 ; Roediger, Putnam, & Smith, 2011 ; Wheeler & Roediger, 1992 ). Hausmann and Vanlehn ( 2007 ) addressed the possibility that generating explanations is beneficial because learners merely spend more time with the content material than learners who are not required to generate an explanation. In their study, they compared the effects of using instructions to self-explain with instructions to merely paraphrase physics (electrodynamics) material. Attending to provided explanations by paraphrasing was not as effective as generating explanations as evidenced by retention scores on an exam 29 days after the experiment and transfer scores within and across domains. Their study concludes, “the important variable for learning was the process of producing an explanation” (p. 423). Thus, we expect benefits from creating either kind of explanation but for the reasons outlined previously, we expect larger benefits from creating visual explanations.

Present experiments

This study set out to answer a number of related questions about the role of learner-generated explanations in learning and understanding of invisible processes. (1) Do students learn more when they generate visual or verbal explanations? We anticipate that learning will be greater with the creation of visual explanations, as they encourage completeness and the integration of structure and function. (2) Does the inclusion of structural and functional information correlate with learning as measured by a post-test? We predict that including greater counts of information, particularly invisible and functional information, will positively correlate with higher post-test scores. (3) Does spatial ability predict the inclusion of structural and functional information in explanations, and does spatial ability predict post-test scores? We predict that high spatial ability participants will include more information in their explanations, and will score higher on post-tests.

Experiment 1

The first experiment examines the effects of creating visual or verbal explanations on the comprehension of a bicycle tire pump’s operation in participants with low and high spatial ability. Although the pump itself is not invisible, the components crucial to its function, notably the inlet and outlet valves, and the movement of air, are located inside the pump. It was predicted that visual explanations would include more information than verbal explanations, particularly structural information, since their construction encourages completeness and the production of a whole mechanical system. It was also predicted that functional information would be biased towards a verbal format, since much of the function of the pump is hidden and difficult to express in pictures. Finally, it was predicted that high spatial ability participants would be able to produce more complete explanations and would thus also demonstrate better performance on the post-test. Explanations were coded for structural and functional content, essential features, invisible features, arrows, and multiple steps.

Participants

Participants were 127 (59 female) seventh and eighth grade students, aged 12–14 years, enrolled in an independent school in New York City. The school’s student body is 70% white, 30% other ethnicities. Approximately 25% of the student body receives financial aid. The sample consisted of three class sections of seventh grade students and three class sections of eighth grade students. Both seventh and eighth grade classes were integrated science (earth, life, and physical sciences) and students were not grouped according to ability in any section. Written parental consent was obtained by means of signed informed consent forms. Each participant was randomly assigned to one of two conditions within each class. There were 64 participants in the visual condition explained the bicycle pump’s function by drawing and 63 participants explained the pump’s function by writing.

The materials consisted of a 12-inch Spalding bicycle pump, a blank 8.5 × 11 in. sheet of paper, and a post-test (Additional file 1 ). The pump’s chamber and hose were made of clear plastic; the handle and piston were black plastic. The parts of the pump (e.g. inlet valve, piston) were labeled.

Spatial ability was assessed using the Vandenberg and Kuse ( 1978 ) mental rotation test (MRT). The MRT is a 20-item test in which two-dimensional drawings of three-dimensional objects are compared. Each item consists of one “target” drawing and four drawings that are to be compared to the target. Two of the four drawings are rotated versions of the target drawing and the other two are not. The task is to identify the two rotated versions of the target. A score was determined by assigning one point to each question if both of the correct rotated versions were chosen. The maximum score was 20 points.

The post-test consisted of 16 true/false questions printed on a single sheet of paper measuring 8.5 × 11 in. Half of the questions related to the structure of the pump and the other half related to its function. The questions were adapted from Heiser and Tversky ( 2002 ) in order to be clear and comprehensible for this age group.

The experiment was conducted over the course of two non-consecutive days during the normal school day and during regularly scheduled class time. On the first day, participants completed the MRT as a whole-class activity. After completing an untimed practice test, they were given 3 min for each of the two parts of the MRT. On the second day, occurring between two and four days after completing the MRT, participants were individually asked to study an actual bicycle tire pump and were then asked to generate explanations of its function. The participants were tested individually in a quiet room away from the rest of the class. In addition to the pump, each participant was one instruction sheet and one blank sheet of paper for their explanations. The post-test was given upon completion of the explanation. The instruction sheet was read aloud to participants and they were instructed to read along. The first set of instructions was as follows: “A bicycle pump is a mechanical device that pumps air into bicycle tires. First, take this bicycle pump and try to understand how it works. Spend as much time as you need to understand the pump.” The next set of instructions differed for participants in each condition. The instructions for the visual condition were as follows: “Then, we would like you to draw your own diagram or set of diagrams that explain how the bike pump works. Draw your explanation so that someone else who has not seen the pump could understand the bike pump from your explanation. Don’t worry about the artistic quality of the diagrams; in fact, if something is hard for you to draw, you can explain what you would draw. What’s important is that the explanation should be primarily visual, in a diagram or diagrams.” The instructions for the verbal condition were as follows: “Then, we would like you to write an explanation of how the bike pump works. Write your explanation so that someone else who has not seen the pump could understand the bike pump from your explanation.” All participants then received these instructions: “You may not use the pump while you create your explanations. Please return it to me when you are ready to begin your explanation. When you are finished with the explanation, you will hand in your explanation to me and I will then give you 16 true/false questions about the bike pump. You will not be able to look at your explanation while you complete the questions.” Study and test were untimed. All students finished within the 45-min class period.

Spatial ability

The mean score on the MRT was 10.56, with a median of 11. Boys scored significantly higher (M = 13.5, SD = 4.4) than girls (M = 8.8, SD = 4.5), F(1, 126) = 19.07, p  < 0.01, a typical finding (Voyer, Voyer, & Bryden, 1995 ). Participants were split into high or low spatial ability by the median. Low and high spatial ability participants were equally distributed in the visual and verbal groups.

Learning outcomes

It was predicted that high spatial ability participants would be better able to mentally animate the bicycle pump system and therefore score higher on the post-test and that post-test scores would be higher for those who created visual explanations. Table  1 shows the scores on the post-test by condition and spatial ability. A two-way factorial ANOVA revealed marginally significant main effect of spatial ability F(1, 124) = 3.680, p  = 0.06, with high spatial ability participants scoring higher on the post-test. There was also a significant interaction between spatial ability and explanation type F(1, 124) = 4.094, p  < 0.01, see Fig.  1 . Creating a visual explanation of the bicycle pump selectively helped low spatial participants.

Post-test scores, by explanation type and spatial ability

Scores on the post-test by condition and spatial ability

Coding explanations

Explanations (see Fig.  2 ) were coded for structural and functional content, essential features, invisible features, arrows, and multiple steps. A subset of the explanations (20%) was coded by the first author and another researcher using the same coding system as a guide. The agreement between scores was above 90% for all measures. Disagreements were resolved through discussion. The first author then scored the remaining explanations.

Examples of visual and verbal explanations of the bicycle pump

Coding for structure and function

A maximum score of 12 points was awarded for the inclusion and labeling of six structural components: chamber, piston, inlet valve, outlet valve, handle, and hose. For the visual explanations, 1 point was given for a component drawn correctly and 1 additional point if the component was labeled correctly. For verbal explanations, sentences were divided into propositions, the smallest unit of meaning in a sentence. Descriptions of structural location e.g. “at the end of the piston is the inlet valve,” or of features of the components, e.g. the shape of a part, counted as structural components. Information was coded as functional if it depicted (typically with an arrow) or described the function/movement of an individual part, or the way multiple parts interact. No explanation contained more than ten functional units.

Visual explanations contained significantly more structural components (M = 6.05, SD = 2.76) than verbal explanations (M = 4.27, SD = 1.54), F(1, 126) = 20.53, p  < 0.05. The number of functional components did not differ between visual and verbal explanations as displayed in Figs.  3 and ​ and4. 4 . Many visual explanations (67%) contained verbal components; the structural and functional information in explanations was coded as depictive or descriptive. Structural and functional information were equally likely to be expressed in words or pictures in visual explanations. It was predicted that explanations created by high spatial participants would include more functional information. However, there were no significant differences found between low spatial (M = 5.15, SD = 2.21) and high spatial (M = 4.62, SD = 2.16) participants in the number of structural units or between low spatial (M = 3.83, SD = 2.51) and high spatial (M = 4.10, SD = 2.13) participants in the number of functional units.

Average number of structural and functional components in visual and verbal explanations

Visual and verbal explanations of chemical bonding

Coding of essential features

To further establish a relationship between the explanations generated and outcomes on the post-test, explanations were also coded for the inclusion of information essential to its function according to a 4-point scale (adapted from Hall et al., 1997 ). One point was given if both the inlet and the outlet valve were clearly present in the drawing or described in writing, 1 point was given if the piston inserted into the chamber was shown or described to be airtight, and 1 point was given for each of the two valves if they were shown or described to be opening/closing in the correct direction.

Visual explanations contained significantly more essential information (M = 1.78, SD = 1.0) than verbal explanations (M = 1.20, SD = 1.21), F(1, 126) = 7.63, p  < 0.05. Inclusion of essential features correlated positively with post-test scores, r = 0.197, p  < 0.05).

Coding arrows and multiple steps

For the visual explanations, three uses of arrows were coded and tallied: labeling a part or action, showing motion, or indicating sequence. Analysis of visual explanations revealed that 87% contained arrows. No significant differences were found between low and high spatial participants’ use of arrows to label and no signification correlations were found between the use of arrows and learning outcomes measured on the post-test.

The explanations were coded for the number of discrete steps used to explain the process of using the bike pump. The number of steps used by participants ranged from one to six. Participants whose explanations, whether verbal or visual, contained multiple steps scored significantly higher (M = 0.76, SD = 0.18) on the post-test than participants whose explanations consisted of a single step (M = 0.67, SD = 0.19), F(1, 126) = 5.02, p  < 0.05.

Coding invisible features

The bicycle tire pump, like many mechanical devices, contains several structural features that are hidden or invisible and must be inferred from the function of the pump. For the bicycle pump the invisible features are the inlet and outlet valves and the three phases of movement of air, entering the pump, moving through the pump, exiting the pump. Each feature received 1 point for a total of 5 possible points.

The mean score for the inclusion of invisible features was 3.26, SD = 1.25. The data were analyzed using linear regression and revealed that the total score for invisible parts significantly predicted scores on the post-test, F(1, 118) = 3.80, p  = 0.05.

In the first experiment, students learned the workings of a bicycle pump from interacting with an actual pump and creating a visual or verbal explanation of its function. Understanding the functionality of a bike pump depends on the actions and consequences of parts that are not visible. Overall, the results provide support for the use of learner-generated visual explanations in developing understanding of a new scientific system. The results show that low spatial ability participants were able to learn as successfully as high spatial ability participants when they first generated an explanation in a visual format.

Visual explanations may have led to greater understanding for a number of reasons. As discussed previously, visual explanations encourage completeness. They force learners to decide on the size, shape, and location of parts/objects. Understanding the “hidden” function of the invisible parts is key to understanding the function of the entire system and requires an understanding of how both the visible and invisible parts interact. The visual format may have been able to elicit components and concepts that are invisible and difficult to integrate into the formation of a mental model. The results show that including more of the essential features and showing multiple steps correlated with superior test performance. Understanding the bicycle pump requires understanding how all of these components are connected through movement, force, and function. Many (67%) of the visual explanations also contained written components to accompany their explanation. Arguably, some types of information may be difficult to depict visually and verbal language has many possibilities that allow for specificity. The inclusion of text as a complement to visual explanations may be key to the success of learner-generated explanations and the development of understanding.

A limitation of this experiment is that participants were not provided with detailed instructions for completing their explanations. In addition, this experiment does not fully clarify the role of spatial ability, since high spatial participants in the visual and verbal groups demonstrated equivalent knowledge of the pump on the post-test. One possibility is that the interaction with the bicycle pump prior to generating explanations was a sufficient learning experience for the high spatial participants. Other researchers (e.g. Flick, 1993 ) have shown that hands-on interactive experiences can be effective learning situations. High spatial ability participants may be better able to imagine the movement and function of a system (e.g. Hegarty, 1992 ).

Experiment 1 examined learning a mechanical system with invisible (hidden) parts. Participants were introduced to the system by being able to interact with an actual bicycle pump. While we did not assess participants’ prior knowledge of the pump with a pre-test, participants were randomly assigned to each condition. The findings have promising implications for teaching. Creating visual explanations should be an effective way to improve performance, especially in low spatial students. Instructors can guide the creation of visual explanations toward the features that augment learning. For example, students can be encouraged to show every step and action and to focus on the essential parts, even if invisible. The coding system shows that visual explanations can be objectively evaluated to provide feedback on students’ understanding. The utility of visual explanations may differ for scientific phenomena that are more abstract, or contain elements that are invisible due to their scale. Experiment 2 addresses this possibility by examining a sub-microscopic area of science: chemical bonding.

Experiment 2

In this experiment, we examine visual and verbal explanations in an area of chemistry: ionic and covalent bonding. Chemistry is often regarded as a difficult subject; one of the essential or inherent features of chemistry which presents difficulty is the interplay between the macroscopic, sub-microscopic, and representational levels (e.g. Bradley & Brand, 1985 ; Johnstone, 1991 ; Taber, 1997 ). In chemical bonding, invisible components engage in complex processes whose scale makes them impossible to observe. Chemists routinely use visual representations to investigate relationships and move between the observable, physical level and the invisible particulate level (Kozma, Chin, Russell, & Marx, 2002 ). Generating explanations in a visual format may be a particularly useful learning tool for this domain.

For this topic, we expect that creating a visual rather than verbal explanation will aid students of both high and low spatial abilities. Visual explanations demand completeness; they were predicted to include more information than verbal explanations, particularly structural information. The inclusion of functional information should lead to better performance on the post-test since understanding how and why atoms bond is crucial to understanding the process. Participants with high spatial ability may be better able to explain function since the sub-microscopic nature of bonding requires mentally imagining invisible particles and how they interact. This experiment also asks whether creating an explanation per se can increase learning in the absence of additional teaching by administering two post-tests of knowledge, one immediately following instruction but before creating an explanation and one after creating an explanation. The scores on this immediate post-test were used to confirm that the visual and verbal groups were equivalent prior to the generation of explanations. Explanations were coded for structural and functional information, arrows, specific examples, and multiple representations. Do the acts of selecting, integrating, and explaining knowledge serve learning even in the absence of further study or teaching?

Participants were 126 (58 female) eighth grade students, aged 13–14 years, with written parental consent and enrolled in the same independent school described in Experiment 1. None of the students previously participated in Experiment 1. As in Experiment 1, randomization occurred within-class, with participants assigned to either the visual or verbal explanation condition.

The materials consisted of the MRT (same as Experiment 1), a video lesson on chemical bonding, two versions of the instructions, the immediate post-test, the delayed post-test, and a blank page for the explanations. All paper materials were typed on 8.5 × 11 in. sheets of paper. Both immediate and delayed post-tests consisted of seven multiple-choice items and three free-response items. The video lesson on chemical bonding consisted of a video that was 13 min 22 s. The video began with a brief review of atoms and their structure and introduced the idea that atoms combine to form molecules. Next, the lesson showed that location in the periodic table reveals the behavior and reactivity of atoms, in particular the gain, loss, or sharing of electrons. Examples of atoms, their valence shell structure, stability, charges, transfer and sharing of electrons, and the formation of ionic, covalent, and polar covalent bonds were discussed. The example of NaCl (table salt) was used to illustrate ionic bonding and the examples of O 2 and H 2 O (water) were used to illustrate covalent bonding. Information was presented verbally, accompanied by drawings, written notes of keywords and terms, and a color-coded periodic table.

On the first of three non-consecutive school days, participants completed the MRT as a whole-class activity. On the second day (occurring between two and three days after completing the MRT), participants viewed the recorded lesson on chemical bonding. They were instructed to pay close attention to the material but were not allowed to take notes. Immediately following the video, participants had 20 min to complete the immediate post-test; all finished within this time frame. On the third day (occurring on the next school day after viewing the video and completing the immediate post-test), the participants were randomly assigned to either the visual or verbal explanation condition. The typed instructions were given to participants along with a blank 8.5 × 11 in. sheet of paper for their explanations. The instructions differed for each condition. For the visual condition, the instructions were as follows: “You have just finished learning about chemical bonding. On the next piece of paper, draw an explanation of how atoms bond and how ionic and covalent bonds differ. Draw your explanation so that another student your age who has never studied this topic will be able to understand it. Be as clear and complete as possible, and remember to use pictures/diagrams only. After you complete your explanation, you will be asked to answer a series of questions about bonding.”

For the verbal condition the instructions were: “You have just finished learning about chemical bonding. On the next piece of paper, write an explanation of how atoms bond and how ionic and covalent bonds differ. Write your explanation so that another student your age who has never studied this topic will be able to understand it. Be as clear and complete as possible. After you complete your explanation, you will be asked to answer a series of questions about bonding.”

Participants were instructed to read the instructions carefully before beginning the task. The participants completed their explanations as a whole-class activity. Participants were given unlimited time to complete their explanations. Upon completion of their explanations, participants were asked to complete the ten-question delayed post-test (comparable to but different from the first) and were given a maximum of 20 min to do so. All participants completed their explanations as well as the post-test during the 45-min class period.

The mean score on the MRT was 10.39, with a median of 11. Boys (M = 12.5, SD = 4.8) scored significantly higher than girls (M = 8.0, SD = 4.0), F(1, 125) = 24.49, p  < 0.01. Participants were split into low and high spatial ability based on the median.

The maximum score for both the immediate and delayed post-test was 10 points. A repeated measures ANOVA showed that the difference between the immediate post-test scores (M = 4.63, SD = 0.469) and delayed post-test scores (M = 7.04, SD = 0.299) was statistically significant F(1, 125) = 18.501, p  < 0.05). Without any further instruction, scores increased following the generation of a visual or verbal explanation. Both groups improved significantly; those who created visual explanations (M = 8.22, SD = 0.208), F(1, 125) = 51.24, p  < 0.01, Cohen’s d  = 1.27 as well as those who created verbal explanations (M = 6.31, SD = 0.273), F(1,125) = 15.796, p  < 0.05, Cohen’s d  = 0.71. As seen in Fig.  5 , participants who generated visual explanations (M = 0.822, SD = 0.208) scored considerably higher on the delayed post-test than participants who generated verbal explanations (M = 0.631, SD = 0.273), F(1, 125) = 19.707, p  < 0.01, Cohen’s d  = 0.88. In addition, high spatial participants (M = 0.824, SD = 0.273) scored significantly higher than low spatial participants (M = 0.636, SD = 0.207), F(1, 125) = 19.94, p  < 0.01, Cohen’s d  = 0.87. The results of the test of the interaction between group and spatial ability was not significant.

Scores on the post-tests by explanation type and spatial ability

Explanations were coded for structural and functional content, arrows, specific examples, and multiple representations. A subset of the explanations (20%) was coded by both the first author and a middle school science teacher with expertise in Chemistry. Both scorers used the same coding system as a guide. The percentage of agreement between scores was above 90 for all measures. The first author then scored the remainder of the explanations. As evident from Fig.  4 , the visual explanations were individual inventions; they neither resembled each other nor those used in teaching. Most contained language, especially labels and symbolic language such as NaCl.

Structure, function, and modality

Visual and verbal explanations were coded for depicting or describing structural and functional components. The structural components included the following: the correct number of valence electrons, the correct charges of atoms, the bonds between non-metals for covalent molecules and between a metal and non-metal for ionic molecules, the crystalline structure of ionic molecules, and that covalent bonds were individual molecules. The functional components included the following: transfer of electrons in ionic bonds, sharing of electrons in covalent bonds, attraction between ions of opposite charge, bonding resulting in atoms with neutral charge and stable electron shell configurations, and outcome of bonding shows molecules with overall neutral charge. The presence of each component was awarded 1 point; the maximum possible points was 5 for structural and 5 for functional information. The modality, visual or verbal, of each component was also coded; if the information was given in both formats, both were coded.

As displayed in Fig.  6 , visual explanations contained a significantly greater number of structural components (M = 2.81, SD = 1.56) than verbal explanations (M = 1.30, SD = 1.54), F(1, 125) = 13.69, p  < 0.05. There were no differences between verbal and visual explanations in the number of functional components. Structural information was more likely to be depicted (M = 3.38, SD = 1.49) than described (M = 0.429, SD = 1.03), F(1, 62) = 21.49, p  < 0.05, but functional information was equally likely to be depicted (M = 1.86, SD = 1.10) or described (M = 1.71, SD = 1.87).

Functional information expressed verbally in the visual explanations significantly predicted scores on the post-test, F(1, 62) = 21.603, p  < 0.01, while functional information in verbal explanations did not. The inclusion of structural information did not significantly predict test scores. As seen Fig.  7 , explanations created by high spatial participants contained significantly more functional components, F(1, 125) = 7.13, p  < 0.05, but there were no ability differences in the amount of structural information created by high spatial participants in either visual or verbal explanations.

Average number of structural and functional components created by low and high spatial ability learners

Ninety-two percent of visual explanations contained arrows. Arrows were used to indicate motion as well as to label. The use of arrows was positively correlated with scores on the post-test, r = 0.293, p  < 0.05. There were no significant differences in the use of arrows between low and high spatial participants.

Specific examples

Explanations were coded for the use of specific examples, such as NaCl, to illustrate ionic bonding and CO 2 and O 2 to illustrate covalent bonding. High spatial participants (M = 1.6, SD = 0.69) used specific examples in their verbal and visual explanations more often than low spatial participants (M = 1.07, SD = 0.79), a marginally significant effect F(1, 125) = 3.65, p  = 0.06. Visual and verbal explanations did not differ in the presence of specific examples. The inclusion of a specific example was positively correlated with delayed test scores, r = 0.555, p  < 0.05.

Use of multiple representations

Many of the explanations (65%) contained multiple representations of bonding. For example, ionic bonding and its properties can be represented at the level of individual atoms or at the level of many atoms bonded together in a crystalline compound. The representations that were coded were as follows: symbolic (e.g. NaCl), atomic (showing structure of atom(s), and macroscopic (visible). Participants who created visual explanations generated significantly more (M =1.79, SD = 1.20) than those who created verbal explanations (M = 1.33, SD = 0.48), F (125) = 6.03, p  < 0.05. However, the use of multiple representations did not significantly correlate with delayed post-test scores on the delayed post-test.

Metaphoric explanations

Although there were too few examples to be included in the statistical analyses, some participants in the visual group created explanations that used metaphors and/or analogies to illustrate the differences between the types of bonding. Figure  4 shows examples of metaphoric explanations. In one example, two stick figures are used to show “transfer” and “sharing” of an object between people. In another, two sharks are used to represent sodium and chlorine, and the transfer of fish instead of electrons.

In the second experiment, students were introduced to chemical bonding, a more abstract and complex set of phenomena than the bicycle pump used in the first experiment. Students were tested immediately after instruction. The following day, half the students created visual explanations and half created verbal explanations. Following creation of the explanations, students were tested again, with different questions. Performance was considerably higher as a consequence of creating either explanation despite the absence of new teaching. Generating an explanation in this way could be regarded as a test of learning. Seen this way, the results echo and amplify previous research showing the advantages of testing over study (e.g. Roediger et al., 2011 ; Roediger & Karpicke, 2006 ; Wheeler & Roediger, 1992 ). Specifically, creating an explanation requires selecting the crucial information, integrating it temporally and causally, and expressing it clearly, processes that seem to augment learning and understanding without additional teaching. Importantly, creating a visual explanation gave an extra boost to learning outcomes over and above the gains provided by creating a verbal explanation. This is most likely due to the directness of mapping complex systems to a visual-spatial format, a format that can also provide a natural check for completeness and coherence as well as a platform for inference. In the case of this more abstract and complex material, generating a visual explanation benefited both low spatial and high spatial participants even if it did not bring low spatial participants up to the level of high spatial participants as for the bicycle pump.

Participants high in spatial ability not only scored better, they also generated better explanations, including more of the information that predicted learning. Their explanations contained more functional information and more specific examples. Their visual explanations also contained more functional information.

As in Experiment 1, qualities of the explanations predicted learning outcomes. Including more arrows, typically used to indicate function, predicted delayed test scores as did articulating more functional information in words in visual explanations. Including more specific examples in both types of explanation also improved learning outcomes. These are all indications of deeper understanding of the processes, primarily expressed in the visual explanations. As before, these findings provide ways that educators can guide students to craft better visual explanations and augment learning.

General discussion

Two experiments examined how learner-generated explanations, particularly visual explanations, can be used to increase understanding in scientific domains, notably those that contain “invisible” components. It was proposed that visual explanations would be more effective than verbal explanations because they encourage completeness and coherence, are more explicit, and are typically multimodal. These two experiments differ meaningfully from previous studies in that the information selected for drawing was not taken from a written text, but from a physical object (bicycle pump) and a class lesson with multiple representations (chemical bonding).

The results show that creating an explanation of a STEM phenomenon benefits learning, even when the explanations are created after learning and in the absence of new instruction. These gains in performance in the absence of teaching bear similarities to recent research showing gains in learning from testing in the absence of new instruction (e.g. Roediger et al., 2011 ; Roediger & Karpicke, 2006 ; Wheeler & Roediger, 1992 ). Many researchers have argued that the retrieval of information required during testing strengthens or enhances the retrieval process itself. Formulating explanations may be an especially effective form of testing for post-instruction learning. Creating an explanation of a complex system requires the retrieval of critical information and then the integration of that information into a coherent and plausible account. Other factors, such as the timing of the creation of the explanations, and whether feedback is provided to students, should help clarify the benefits of generating explanations and how they may be seen as a form of testing. There may even be additional benefits to learners, including increasing their engagement and motivation in school, and increasing their communication and reasoning skills (Ainsworth, Prain, & Tytler, 2011 ). Formulating a visual explanation draws upon students’ creativity and imagination as they actively create their own product.

As in previous research, students with high spatial ability both produced better explanations and performed better on tests of learning (e.g. Uttal et al., 2013 ). The visual explanations of high spatial students contained more information and more of the information that predicts learning outcomes. For the workings of a bicycle pump, creating a visual as opposed to verbal explanation had little impact on students of high spatial ability but brought students of lower spatial ability up to the level of students with high spatial abilities. For the more difficult set of concepts, chemical bonding, creating a visual explanation led to much larger gains than creating a verbal one for students both high and low in spatial ability. It is likely a mistake to assume that how and high spatial learners will remain that way; there is evidence that spatial ability develops with experience (Baenninger & Newcombe, 1989 ). It is possible that low spatial learners need more support in constructing explanations that require imagining the movement and manipulation of objects in space. Students learned the function of the bike pump by examining an actual pump and learned bonding through a video presentation. Future work to investigate methods of presenting material to students may also help to clarify the utility of generating explanations.

Creating visual explanations had greater benefits than those accruing from creating verbal ones. Surely some of the effectiveness of visual explanations is because they represent and communicate more directly than language. Elements of a complex system can be depicted and arrayed spatially to reflect actual or metaphoric spatial configurations of the system parts. They also allow, indeed, encourage, the use of well-honed spatial inferences to substitute for and support abstract inferences (e.g. Larkin & Simon, 1987 ; Tversky, 2011 ). As noted, visual explanations provide checks for completeness and coherence, that is, verification that all the necessary elements of the system are represented and that they work together properly to produce the outcomes of the processes. Visual explanations also provide a concrete reference for making and checking inferences about the behavior, causality, and function of the system. Thus, creating a visual explanation facilitates the selection and integration of information underlying learning even more than creating a verbal explanation.

Creating visual explanations appears to be an underused method of supporting and evaluating students’ understanding of dynamic processes. Two obstacles to using visual explanations in classrooms seem to be developing guidelines for creating visual explanations and developing objective scoring systems for evaluating them. The present findings give insights into both. Creating a complete and coherent visual explanation entails selecting the essential components and linking them by behavior, process, or causality. This structure and organization is familiar from recipes or construction sets: first the ingredients or parts, then the sequence of actions. It is also the ingredients of theater or stories: the players and their actions. In fact, the creation of visual explanations can be practiced on these more familiar cases and then applied to new ones in other domains. Deconstructing and reconstructing knowledge and information in these ways has more generality than visual explanations: these techniques of analysis serve thought and provide skills and tools that underlie creative thought. Next, we have shown that objective scoring systems can be devised, beginning with separating the information into structure and function, then further decomposing the structure into the central parts or actors and the function into the qualities of the sequence of actions and their consequences. Assessing students’ prior knowledge and misconceptions can also easily be accomplished by having students create explanations at different times in a unit of study. Teachers can see how their students’ ideas change and if students can apply their understanding by analyzing visual explanations as a culminating activity.

Creating visual explanations of a range of phenomena should be an effective way to augment students’ spatial thinking skills, thereby increasing the effectiveness of these explanations as spatial ability increases. The proverbial reading, writing, and arithmetic are routinely regarded as the basic curriculum of school learning and teaching. Spatial skills are not typically taught in schools, but should be: these skills can be learned and are essential to functioning in the contemporary and future world (see Uttal et al., 2013 ). In our lives, both daily and professional, we need to understand the maps, charts, diagrams, and graphs that appear in the media and public places, with our apps and appliances, in forms we complete, in equipment we operate. In particular, spatial thinking underlies the skills needed for professional and amateur understanding in STEM fields and knowledge and understanding STEM concepts is increasingly required in what have not been regarded as STEM fields, notably the largest employers, business, and service.

This research has shown that creating visual explanations has clear benefits to students, both specific and potentially general. There are also benefits to teachers, specifically, revealing misunderstandings and gaps in knowledge. Visualizations could be used by teachers as a formative assessment tool to guide further instructional activities and scoring rubrics could allow for the identification of specific misconceptions. The bottom line is clear. Creating a visual explanation is an excellent way to learn and master complex systems.

Additional file

Post-tests. (DOC 44 kb)

Acknowledgments

The authors are indebted to the Varieties of Understanding Project at Fordham University and The John Templeton Foundation and to the following National Science Foundation grants for facilitating the research and/or preparing the manuscript: National Science Foundation NSF CHS-1513841, HHC 0905417, IIS-0725223, IIS-0855995, and REC 0440103. We are grateful to James E. Corter for his helpful suggestions and to Felice Frankel for her inspiration. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the funders. Please address correspondence to Barbara Tversky at the Columbia Teachers College, 525 W. 120th St., New York, NY 10025, USA. Email: [email protected].

Authors’ contributions

This research was part of EB’s doctoral dissertation under the advisement of BT. Both authors contributed to the design, analysis, and drafting of the manuscript. Both authors read and approved the final manuscript.

Competing interests

The author declares that they have no competing interests.

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5. Visual Representation

How can you design computer displays that are as meaningful as possible to human viewers? Answering this question requires understanding of visual representation - the principles by which markings on a surface are made and interpreted. The analysis in this article addresses the most important principles of visual representation for screen design, introduced with examples from the early history of graphical user interfaces . In most cases, these principles have been developed and elaborated within whole fields of study and professional skill - typography , cartography, engineering and architectural draughting, art criticism and semiotics. Improving on the current conventions requires serious skill and understanding. Nevertheless, interaction designers should be able, when necessary, to invent new visual representations.

Introduction to Visual Representation by Alan Blackwell

Alan Blackwell on applying theories of Visual Representation

• 5.1 Typography and text

For many years, computer displays resembled paper documents. This does not mean that they were simplistic or unreasonably constrained. On the contrary, most aspects of modern industrial society have been successfully achieved using the representational conventions of paper, so those conventions seem to be powerful ones. Information on paper can be structured using tabulated columns, alignment, indentation and emphasis, borders and shading. All of those were incorporated into computer text displays. Interaction conventions, however, were restricted to operations of the typewriter rather than the pencil. Each character typed would appear at a specific location. Locations could be constrained, like filling boxes on a paper form. And shortcut command keys could be defined using onscreen labels or paper overlays. It is not text itself, but keyboard interaction with text that is limited and frustrating compared to what we can do with paper (Sellen and Harper 2001).

But despite the constraints on keyboard interaction, most information on computer screens is still represented as text. Conventions of typography and graphic design help us to interpret that text as if it were on a page, and human readers benefit from many centuries of refinement in text document design. Text itself, including many writing systems as well as specialised notations such as algebra, is a visual representation that has its own research and educational literature. Documents that contain a mix of bordered or coloured regions containing pictures, text and diagrammatic elements can be interpreted according to the conventions of magazine design, poster advertising, form design, textbooks and encyclopaedias. Designers of screen representations should take care to properly apply the specialist knowledge of those graphic and typographic professions. Position on the page, use of typographic grids, and genre-specific illustrative conventions should all be taken into account.

Author/Copyright holder: Unknown (pending investigation). Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.1 : Contemporary example from the grid system website

Figure 5.2 : Example of a symbolic algebra expression (the single particle solution to Schrodinger's equation)

Figure 5.3 : Table layout of funerals from the plague in London in 1665

Figure 5.4 : Tabular layout of the first page of the Gutenberg Bible: Volume 1, Old Testament, Epistle of St. Jerome. The Gutenberg Bible was printed by Johannes Gutenberg, in Mainz, Germany in the 1450s

• 5.1.1 Summary

Most screen-based information is interpreted according to textual and typographic conventions, in which graphical elements are arranged within a visual grid, occasionally divided or contained with ruled and coloured borders. Where to learn more:

thegridsystem.org

Resnick , Elizabeth (2003): Design for Communication: Conceptual Graphic Design Basics. Wiley

• 5.2 Maps and graphs

The computer has, however, also acquired a specialised visual vocabulary and conventions. Before the text-based computer terminal (or 'glass teletype') became ubiquitous, cathode ray tube displays were already used to display oscilloscope waves and radar echoes. Both could be easily interpreted because of their correspondence to existing paper conventions. An oscilloscope uses a horizontal time axis to trace variation of a quantity over time, as pioneered by William Playfair in his 1786 charts of the British economy. A radar screen shows direction and distance of objects from a central reference point, just as the Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem. Many visual displays on computers continue to use these ancient but powerful inventions - the map and the graph. In particular, the first truly large software project, the SAGE air defense system, set out to present data in the form of an augmented radar screen - an abstract map, on which symbols and text could be overlaid. The first graphics computer, the Lincoln Laboratory Whirlwind, was created to show maps, not text.

Figure 5.5 : The technique invented by William Playfair, for visual representation of time series data.

Author/Copyright holder: Courtesy of Premek. V. Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.6 : Time series data as shown on an oscilloscope screen

Author/Copyright holder: Courtesy of Remi Kaupp. Copyright terms and licence: CC-Att-SA (Creative Commons Attribution-ShareAlike 3.0 Unported)

Figure 5.7 : Early radar screen from HMS Belfast built in 1936

Author/Copyright holder: Courtesy of NOAA's National Weather Service. Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.8 : Early weather radar - Hurricane Abby approaching the coast of British Honduras in 1960

Figure 5.9 : The Hereford Mappa Mundi of 1300 organised places according to their approximate direction and distance from Jerusalem

Author/Copyright holder: Courtesy of Wikipedia. Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.10 : The SAGE system in use. The SAGE system used light guns as interaction devices.

Author/Copyright holder: The MITRE Corporation. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.11 : The Whirlwind computer at the MIT Lincoln Laboratory

• 5.2.1 Summary

Basic diagrammatic conventions rely on quantitative correspondence between a direction on the surface and a continuous quantity such as time or distance. These should follow established conventions of maps and graphs.

Where to learn more:

MacEachren , Alan M. (2004): How Maps Work: Representation, Visualization, and Design. The Guilford Press

• 5.3 Schematic drawings

Ivan Sutherland's groundbreaking PhD research with Whirlwind's successor TX-2 introduced several more sophisticated alternatives (Sutherland 1963). The use of a light pen allowed users to draw arbitrary lines, rather than relying on control keys to select predefined options. An obvious application, in the engineering context of Massachusetts Institute of Technology (MIT) where Sutherland worked, was to make engineering drawings such as the girder bridge in Figure 13. Lines on the screen are scaled versions of the actual girders, and text information can be overlaid to give details of force calculations. Plans of this kind, as a visual representation, are closely related to maps. However, where the plane of a map corresponds to a continuous surface, engineering drawings need not be continuous. Each set of connected components must share the same scale, but white space indicates an interpretive break, so that independent representations can potentially share the same divided surface - a convention introduced in Diderot's encyclopedia of 1772, which showed pictures of multiple objects on a page, but cut them loose from any shared pictorial context.

Author/Copyright holder: Courtesy of Ivan Sutherland. Copyright terms and licence: CC-Att-SA-3 (Creative Commons Attribution-ShareAlike 3.0).

Figure 5.12 : The TX-2 graphics computer, running Ivan Sutherland's Sketchpad software

Figure 5.13 : An example of a force diagram created using Sutherland's Sketchpad

Figure 5.14 : A page from the Encyclopédie of Diderot and d'Alembert, combining pictorial elements with diagrammatic lines and categorical use of white space.

• 5.3.1 Summary

Engineering drawing conventions allow schematic views of connected components to be shown in relative scale, and with text annotations labelling the parts. White space in the representation plane can be used to help the reader distinguish elements from each other rather than directly representing physical space. Where to learn more:

Engineering draughting textbooks

Ferguson , Eugene S. (1994): Engineering and the Mind's Eye. MIT Press

• 5.4 Pictures

The examples so far may seem rather abstract. Isn't the most 'natural' visual representation simply a picture of the thing you are trying to represent? In that case, what is so hard about design? Just point a camera, and take the picture. It seems like pictures are natural and intuitive, and anyone should be able to understand what they mean. Of course, you might want the picture to be more or less artistic, but that isn't a technical concern, is it? Well, Ivan Sutherland also suggested the potential value that computer screens might offer as artistic tools. His Sketchpad system was used to create a simple animated cartoon of a winking girl. We can use this example to ask whether pictures are necessarily 'natural', and what design factors are relevant to the selection or creation of pictures in an interaction design context.

We would not describe Sutherland's girl as 'realistic', but it is an effective representation of a girl. In fact, it is an unusually good representation of a winking girl, because all the other elements of the picture are completely abstract and generic. It uses a conventional graphic vocabulary of lines and shapes that are understood in our culture to represent eyes, mouths and so on - these elements do not draw attention to themselves, and therefore highlight the winking eye. If a realistic picture of an actual person was used instead, other aspects of the image (the particular person) might distract the viewer from this message.

Figure 5.15 : Sutherland's 'Winking Girl' drawing, created with the Sketchpad system

It is important, when considering the design options for pictures, to avoid the 'resemblance fallacy', i.e. that drawings are able to depict real object or scenes because the viewer's perception of the flat image simulates the visual perception of a real scene. In practice, all pictures rely on conventions of visual representation, and are relatively poor simulations of natural engagement with physical objects, scenes and people. We are in the habit of speaking approvingly of some pictures as more 'realistic' than others (photographs, photorealistic ray-traced renderings, 'old master' oil paintings), but this simply means that they follow more rigorously a particular set of conventions. The informed designer is aware of a wide range of pictorial conventions and options.

As an example of different pictorial conventions, consider the ways that scenes can be rendered using different forms of artistic perspective. The invention of linear perspective introduced a particular convention in which the viewer is encouraged to think of the scene as perceived through a lens or frame while holding his head still, so that nearby objects occupy a disproportionate amount of the visual field. Previously, pictorial representations more often varied the relative size of objects according to their importance - a kind of 'semantic' perspective. Modern viewers tend to think of the perspective of a camera lens as being most natural, due to the ubiquity of photography, but we still understand and respect alternative perspectives, such as the isometric perspective of the pixel art group eBoy, which has been highly influential on video game style.

Author/Copyright holder: Courtesy of Masaccio (1401-1428). Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship))

Figure 5.16 : Example of an early work by Masaccio, demonstrating a 'perspective' in which relative size shows symbolic importance

Author/Copyright holder: eBoy.com. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.17 : Example of the strict isometric perspective used by the eBoy group

Author/Copyright holder: Courtesy of Masaccio (1401-1428). Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.18 : Masaccio's mature work The Tribute Money, demonstrating linear perspective

As with most conventions of pictorial representation, new perspective rendering conventions are invented and esteemed for their accuracy by critical consensus, and only more slowly adopted by untrained readers. The consensus on preferred perspective shifts across cultures and historical periods. It would be naïve to assume that the conventions of today are the final and perfect product of technical evolution. As with text, we become so accustomed to interpreting these representations that we are blind to the artifice. But professional artists are fully aware of the conventions they use, even where they might have mechanical elements - the way that a photograph is framed changes its meaning, and a skilled pencil drawing is completely unlike visual edge-detection thresholds. A good pictorial representation need not simulate visual experience any more than a good painting of a unicorn need resemble an actual unicorn. When designing user interfaces, all of these techniques are available for use, and new styles of pictorial rendering are constantly being introduced.

• 5.4.1 Summary

Pictorial representations, including line drawings, paintings, perspective renderings and photographs rely on shared interpretive conventions for their meaning. It is naïve to treat screen representations as though they were simulations of experience in the physical world. Where to learn more:

Micklewright , Keith (2005): Drawing: Mastering the Language of Visual Expression. Harry N. Abrams

Stroebel , Leslie, Todd , Hollis and Zakia , Richard (1979): Visual Concepts for Photographers. Focal Press

• 5.5 Node-and-link diagrams

The first impulse of a computer scientist, when given a pencil, seems to be to draw boxes and connect them with lines. These node and link diagrams can be analysed in terms of the graph structures that are fundamental to the study of algorithms (but unrelated to the visual representations known as graphs or charts). A predecessor of these connectivity diagrams can be found in electrical circuit schematics, where the exact location of components, and the lengths of the wires, can be arranged anywhere, because they are irrelevant to the circuit function. Another early program created for the TX-2, this time by Ivan Sutherland's brother Bert, allowed users to create circuit diagrams of this kind. The distinctive feature of a node-and-link connectivity diagram is that, since the position of each node is irrelevant to the operation of the circuit, it can be used to carry other information. Marian Petre's research into the work of electronics engineers (Petre 1995) catalogued the ways in which they positioned components in ways that were meaningful to human readers, but not to the computer - like the blank space between Diderot's objects this is a form of 'secondary notation' - use of the plane to assist the reader in ways not related to the technical content.

Circuit connectivity diagrams have been most widely popularised through the London Underground diagram, an invention of electrical engineer Henry Beck. The diagram clarified earlier maps by exploiting the fact that most underground travellers are only interested in order and connectivity, not location, of the stations on the line. (Sadly, the widespread belief that a 'diagram' will be technical and hard to understand means that most people describe this as the London Undergound 'map', despite Beck's insistence on his original term).

Author/Copyright holder: Courtesy of Harry C. Beck and possibly F. H. Stingemore, born 1890, died 1954. Stingmore designed posters for the Underground Group and London Transport 1914-1942. Copyright terms and licence: Unknown (pending investigation). See section "Exceptions" in the copyright terms below.

Figure 5.19 : Henry Beck's London Underground Diagram (1933)

Author/Copyright holder: Computer History Museum, Mountain View, CA, USA. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.20 : Node and link diagram of the kind often drawn by computing professionals

Figure 5.21 : Map of the London Underground network, as it was printed before the design of Beck's diagram (1932)

• 5.5.1 Summary

Node and link diagrams are still widely perceived as being too technical for broad acceptance. Nevertheless, they can present information about ordering and relationships clearly, especially if consideration is given to the value of allowing human users to specify positions. Where to learn more:

Diagrammatic representation books

Lowe , Ric (1992): Successful Instructional Diagram.

• 5.6 Icons and symbols

Maps frequently use symbols to indicate specific kinds of landmark. Sometimes these are recognisably pictorial (the standard symbols for tree and church), but others are fairly arbitrary conventions (the symbol for a railway station). As the resolution of computer displays increased in the 1970s, a greater variety of symbols could be differentiated, by making them more detailed, as in the MIT SDMS (Spatial Data Management System) that mapped a naval battle scenario with symbols for different kinds of ship. However, the dividing line between pictures and symbols is ambiguous. Children's drawings of houses often use conventional symbols (door, four windows, triangle roof and chimney) whether or not their own house has two storeys, or a fireplace. Letters of the Latin alphabet are shapes with completely arbitrary relationship to their phonetic meaning, but the Korean phonetic alphabet is easier to learn because the forms mimic the shape of the mouth when pronouncing those sounds. The field of semiotics offers sophisticated ways of analysing the basis on which marks correspond to meanings. In most cases, the best approach for an interaction designer is simply to adopt familiar conventions. When these do not exist, the design task is more challenging.

It is unclear which of the designers working on the Xerox Star coined the term 'icon' for the small pictures symbolising different kinds of system object. David Canfield Smith winningly described them as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts. But 'icon' is also used as a technical term in semiotics. Unfortunately, few of the Xerox team had a sophisticated understanding of semiotics. It was fine art PhD Susan Kare's design work on the Apple Macintosh that established a visual vocabulary which has informed the genre ever since. Some general advice principles are offered by authors such as Horton (1994), but the successful design of icons is still sporadic. Many software publishers simply opt for a memorable brand logo, while others seriously misjudge the kinds of correspondence that are appropriate (my favourite blooper was a software engineering tool in which a pile of coins was used to access the 'change' command).

It has been suggested that icons, being pictorial, are easier to understand than text, and that pre-literate children, or speakers of different languages, might thereby be able to use computers without being able to read. In practice, most icons simply add decoration to text labels, and those that are intended to be self-explanatory must be supported with textual tooltips. The early Macintosh icons, despite their elegance, were surprisingly open to misinterpretation. One PhD graduate of my acquaintance believed that the Macintosh folder symbol was a briefcase (the folder tag looked like a handle), which allowed her to carry her files from place to place when placed inside it. Although mistaken, this belief never caused her any trouble - any correspondence can work, so long as it is applied consistently.

Copyright terms and licence: pd (Public Domain (information that is common property and contains no original authorship)).

Figure 5.22 : In art, the term Icon (from Greek, eikon, "image") commonly refers to religious paintings in Eastern Orthodox, Oriental Orthodox, and Eastern-rite Catholic jurisdictions. Here a 6th-century encaustic icon from Saint Catherine's Monastery, Mount Sinai

Author/Copyright holder: Apple Computer, Inc. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.23 : In computing, David Canfield Smith described computer icons as being like religious icons, which he said were pictures standing for (abstract) spiritual concepts.

• 5.6.1 Summary

The design of simple and memorable visual symbols is a sophisticated graphic design skill. Following established conventions is the easiest option, but new symbols must be designed with an awareness of what sort of correspondence is intended - pictorial, symbolic, metonymic (e.g. a key to represent locking), bizarrely mnemonic, but probably not monolingual puns. Where to learn more:

Napoles , Veronica (1987): Corporate Identity Design.

• 5.7 Visual metaphor

The ambitious graphic designs of the Xerox Star/Alto and Apple Lisa/Macintosh were the first mass-market visual interfaces. They were marketed to office professionals, making the 'cover story' that they resembled an office desktop a convenient explanatory device. Of course, as was frequently noted at the time, these interfaces behaved nothing like a real desktop. The mnemonic symbol for file deletion (a wastebasket) was ridiculous if interpreted as an object placed on a desk. And nobody could explain why the desk had windows in it (the name was derived from the 'clipping window' of the graphics architecture used to implement them - it was at some later point that they began to be explained as resembling sheets of paper on a desk). There were immediate complaints from luminaries such as Alan Kay and Ted Nelson that strict analogical correspondence to physical objects would become obstructive rather than instructive. Nevertheless, for many years the marketing story behind the desktop metaphor was taken seriously, despite the fact that all attempts to improve the Macintosh design with more elaborate visual analogies , as in General Magic and Microsoft Bob, subsequently failed.

The 'desktop' can be far more profitably analysed (and extended) by understanding the representational conventions that it uses. The size and position of icons and windows on the desktop has no meaning, they are not connected, and there is no visual perspective, so it is neither a map, graph nor picture. The real value is the extent to which it allows secondary notation, with the user creating her own meaning by arranging items as she wishes. Window borders separate areas of the screen into different pictorial, text or symbolic contexts as in the typographic page design of a textbook or magazine. Icons use a large variety of conventions to indicate symbolic correspondence to software operations and/or company brands, but they are only occasionally or incidentally organised into more complex semiotic structures.

Author/Copyright holder:Apple Computer, Inc and Computer History Museum, Mountain View, CA. Copyright terms and licence: All Rights Reserved. Reproduced with permission. See section "Exceptions" in the copyright terms below.

Figure 5.24 : Apple marketed the visual metaphor in 1983 as a key benefit of the Lisa computer. This advertisement said 'You can work with Lisa the same familiar way you work at your desk'. However a controlled study by Carroll and Mazur (1986) found that the claim for immediately familiar operation may have been exaggerated.

Figure 5.25 : The Xerox Alto and Apple Lisa, early products in which bitmapped displays allowed pictorial icons to be used as mnemonic cues within the 'desktop metaphor'

Author/Copyright holder: Courtesy of Mschlindwein. Copyright terms and licence: CC-Att-SA (Creative Commons Attribution-ShareAlike 3.0 Unported).

Figure 5.26 : Apple Lisa

• 5.7.1 Summary

Theories of visual representation, rather than theories of visual metaphor, are the best approach to explaining the conventional Macintosh/Windows 'desktop'. There is huge room for improvement. Where to learn more:

Blackwell , Alan (2006): The reification of metaphor as a design tool . In ACM Transactions on Computer-Human Interaction , 13 (4) pp. 490-530

• 5.8 Unified theories of visual representation

The analysis in this article has addressed the most important principles of visual representation for screen design, introduced with examples from the early history of graphical user interfaces. In most cases, these principles have been developed and elaborated within whole fields of study and professional skill - typography, cartography, engineering and architectural draughting, art criticism and semiotics. Improving on the current conventions requires serious skill and understanding. Nevertheless, interaction designers should be able, when necessary, to invent new visual representations.

One approach is to take a holistic perspective on visual language, information design, notations, or diagrams. Specialist research communities in these fields address many relevant factors from low-level visual perception to critique of visual culture. Across all of them, it can be necessary to ignore (or not be distracted by) technical and marketing claims, and to remember that all visual representations simply comprise marks on a surface that are intended to correspond to things understood by the reader. The two dimensions of the surface can be made to correspond to physical space (in a map), to dimensions of an object, to a pictorial perspective, or to continuous abstract scales (time or quantity). The surface can also be partitioned into regions that should be interpreted differently. Within any region, elements can be aligned, grouped, connected or contained in order to express their relationships. In each case, the correspondence between that arrangement, and the intended interpretation, must be understood by convention, explained, or derived from the structural and perceptual properties of marks on the plane. Finally, any individual element might be assigned meaning according to many different semiotic principles of correspondence.

The following table summarises holistic views, as introduced above, drawing principally on the work of Bertin, Richards, MacEachren, Blackwell & Engelhardt and Engelhardt. Where to learn more:

Engelhardt , Yuri (2002). The Language of Graphics. A framework for the analysis of syntax and meaning in maps, charts and diagrams (PhD Thesis) . University of Amsterdam

Table 5.1 : Summary of the ways in which graphical representations can be applied in design, via different systems of correspondence

Table 5.2 : Screenshot from the site gapminder.org, illustrating a variety of correspondence conventions used in different parts of the page

As an example of how one might analyse (or working backwards, design) a complex visual representation, consider the case of musical scores. These consist of marks on a paper surface, bound into a multi-page book, that is placed on a stand at arms length in front of a performer. Each page is vertically divided into a number of regions, visually separated by white space and grid alignment cues. The regions are ordered, with that at the top of the page coming first. Each region contains two quantitative axes, with the horizontal axis representing time duration, and the vertical axis pitch. The vertical axis is segmented by lines to categorise pitch class . Symbols placed at a given x-y location indicate a specific pitched sound to be initiated at a specific time. A conventional symbol set indicates the duration of the sound. None of the elements use any variation in colour, saturation or texture. A wide variety of text labels and annotation symbols are used to elaborate these basic elements. Music can be, and is, also expressed using many other visual representations (see e.g. Duignan for a survey of representations used in digital music processing).

• 5.9 Where to learn more

The historical examples of early computer representations used in this article are mainly drawn from Sutherland (Ed. Blackwell and Rodden 2003), Garland (1994), and Blackwell (2006). Historical reviews of visual representation in other fields include Ferguson (1992), Pérez-Gómez and Pelletier (1997), McCloud (1993), Tufte (1983). Reviews of human perceptual principles can be found in Gregory (1970), Ittelson (1996), Ware (2004), Blackwell (2002). Advice on principles of interaction with visual representation is distributed throughout the HCI literature, but classics include Norman (1988), Horton (1994), Shneiderman ( Shneiderman and Plaisant 2009, Card et al 1999, Bederson and Shneiderman 2003) and Spence (2001). Green's Cognitive Dimensions of Notations framework has for many years provided a systematic classification of the design parameters in interactive visual representations. A brief introduction is provided in Blackwell and Green (2003).

Research on visual representation topics is regularly presented at the Diagrams conference series (which has a particular emphasis on cognitive science ), the InfoDesign and Vision Plus conferences (which emphasise graphic and typographic information design), the Visual Languages and Human-Centric Computing symposia (emphasising software tools and development), and the InfoVis and Information Visualisation conferences (emphasising quantitative and scientific data visualisation ).

• 5.9.0.1 IV - International Conference on Information Visualization

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998

• 5.9.0.2 DIAGRAMS - International Conference on the Theory and Application of Diagrams

2008 2006 2004 2002 2000

• 5.9.0.3 VL-HCC - Symposium on Visual Languages and Human Centric Computing

2008 2007 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990

• 5.9.0.4 InfoVis - IEEE Symposium on Information Visualization

2005 2004 2003 2002 2001 2000 1999 1998 1997 1995

• 5.10 References

Anderson , Michael, Meyer , Bernd and Olivier , Patrick (2002): Diagrammatic Representation and Reasoning. London, UK,

Bederson , Benjamin B. and Shneiderman , Ben (2003): The Craft of Information Visualization : Readings and Reflections. Morgan Kaufman Publishers

Bertin , Jacques (1967): Semiology of Graphics: Diagrams, Networks, Maps (Sémiologie graphique: Les diagrammes - Les réseaux - Les cartes). English translation by W. J. Berg. Madison, WI, USA, University of Wisconsin Press

Blackwell , Alan (2002): Psychological perspectives on diagrams and their users. In: Anderson , Michael, Meyer , Bernd and Olivier , Patrick (eds.). "Diagrammatic Representation and Reasoning". London, UK: pp. 109-123

Blackwell , Alan and Engelhardt , Yuri (2002): A Meta-Taxonomy for Diagram Research. In: Anderson , Michael, Meyer , Bernd and Olivier , Patrick (eds.). "Diagrammatic Representation and Reasoning". London, UK: pp. 47-64

Blackwell , Alan and Green , T. R. G. (2003): Notational Systems - The Cognitive Dimensions of Notations Framework. In: Carroll , John M. (ed.). "HCI Models, Theories, and Frameworks". San Francisco: Morgan Kaufman Publisherspp. 103-133

Carroll , John M. and Mazur , Sandra A. (1986): LisaLearning . In Computer , 19 (11) pp. 35-49

Garland , Ken (1994): Mr . Beck's Underground Map. Capital Transport Publishing

Goodman , Nelson (1976): Languages of Art. Hackett Publishing Company

Gregory , Richard L. (1970): The Intelligent Eye. London, Weidenfeld and Nicolson

Horton , William (1994): The Icon Book: Visual Symbols for Computer Systems and Documentation. John Wiley and Sons

Ittelson , W. H. (1996): Visual perception of markings . In Psychonomic Bulletin & Review , 3 (2) pp. 171-187

Mccloud , Scott (1994): Understanding Comics: The Invisible Art. Harper Paperbacks

Norman , Donald A. (1988): The Design of Everyday Things. New York, Doubleday

Petre , Marian (1995): Why Looking Isn't Always Seeing: Readership Skills and Graphical Programming . In Communications of the ACM , 38 (6) pp. 33-44

Pérez-Gómez , Alberto and Pelletier , Louise (1997): Architectural Representation and the Perspective Hinge. MIT Press

Richards , Clive (1984). Diagrammatics: an investigation aimed at providing a theoretical framework for studying diagrams and for establishing a taxonomy of their fundamental modes of graphic organization. Unpublished Phd Thesis . Royal College of Art, London, UK

Sellen , Abigail and Harper , Richard H. R. (2001): The Myth of the Paperless Office. MIT Press

Shneiderman , Ben and Plaisant , Catherine (2009): Designing the User Interface : Strategies for Effective Human-Computer Interaction (5th ed.). Addison-Wesley

Spence , Robert (2001): Information Visualization. Addison Wesley

Sutherland , Ivan E. (1963). Sketchpad, A Man-Machine Graphical Communication System. PhD Thesis at Massachusetts Institute of Technology, online version and editors' introduction by Alan Blackwell & K. Rodden. Technical Report 574 . Cambridge University Computer Laboratory

Tufte , Edward R. (1983): The Visual Display of Quantitative Information. Cheshire, CT , Graphics Press

Ware , Colin (2004): Information Visualization: Perception for Design, 2nd Ed. San Francisco, Morgan Kaufman

• 5 Visual Representation

Human-Computer Interaction: The Foundations of UX Design

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5.10 commentary by ben shneiderman.

Since computer displays are such powerful visual appliances, careful designers devote extensive effort to getting the visual representation right. They have to balance the demands of many tasks, diverse users, and challenging requirements, such as short learning time, rapid performance, low error rates, and good retention over time. Designing esthetic interfaces that please and even delight users is a further expectation that designers must meet to be successful. For playful and discretionary tasks esthetic concerns may dominate, but for life critical tasks, rapid performance with low error rates are essential. Alan Blackwell's competent description of many visual representation issues is a great start for newcomers with helpful reminders even for experienced designers. The videos make for a pleasant personal accompaniment that bridges visual representation for interface design with thoughtful analyses of representational art. Blackwell's approach might be enriched by more discussion of visual representations in functional product design tied to meaningful tasks. Learning from paintings of Paris is fine, but aren't there other lessons to learn from visual representations in airport kiosks, automobile dashboards, or intensive care units? These devices as well as most graphical user interfaces and mobile devices raise additional questions of changing state visualization and interaction dynamics. Modern designers need to do more than show the right phone icon, they need to show ringing, busy, inactive, no network, conference mode, etc., which may include color changes (highlighted, grayed out), animations, and accompanying sounds. These designers also need to deal with interactive visual representations that happen with a click, double-click, right-click, drag, drag-and-drop, hover, multi-select, region-select, brushing-linking, and more. The world of mobile devices such as phones, cameras, music players, or medical sensors is the new frontier for design, where visual representations are dynamic and tightly integrated with sound, haptics, and novel actions such as shaking, twisting, or body movements. Even more challenging is the expectation that goes beyond the solitary viewer to the collaboration in which multiple users embedded in a changing physical environment produce new visual representations. These changing and interactive demands on designers invite creative expressions that are very different from designs for static signs, printed diagrams, or interpretive art. The adventure for visual representation designers is to create a new language of interaction that engages users, accelerates learning, provides comprehensible feedback, and offers appropriate warnings when dangers emerge. Blackwell touches on some of these issues in the closing Gapminder example, but I was thirsty for more.

5.11 Commentary by Clive Richards

If I may be permitted a graphically inspired metaphor Alan Blackwell provides us with a neat pen sketch of that extensive scene called 'visual representation' (Blackwell 2011).

"Visualisation has a lot more to offer than most people are aware of today" we are told by Robert Kosara at the end of his commentary (Kosara 2010) on Stephen Few's related article on ' Data visualisation for human perception ' (Few 2010). Korsara is right, and Blackwell maps out the broad territory in which many of these visualisation offerings may be located. In this commentary I offer a few observations on some prominent features in that landscape: dynamics, picturing, semiotics and metaphor.

Ben Shneiderman's critique of Blackwell's piece points to a lack of attention to "... additional questions of changing state visualisations and interaction dynamics" (Shneiderman 2010). Indeed the possibilities offered by these additional questions present some exciting challenges for interaction designers - opportunities to create novel and effective combinations of visual with other sensory and motor experiences in dynamic operational contexts. Shneiderman suggests that: "These changing and interactive demands on designers invite creative expressions that are very different from design for static signs, printed diagrams, or interpretive art". This may be so up to a point, but here Shneinderman and I part company a little. The focus of Blackwell's essay is properly on the visual representation side of facilities available to interaction designers, and in that context he is quite right to give prominence to highly successful but static visual representation precedents, and also to point out the various specialist fields of endeavour in which they have been developed. Some of these representational approaches have histories reaching back thousands of years and are deeply embedded within our culture. It would be foolhardy to disregard conventions established in, say, the print domain, and to try to re-invent everything afresh for the screen, even if this were a practical proposition. Others have made arguments to support looking to historical precedents. For example Michael Twyman has pointed out that when considering typographic cueing and "... the problems of the electronic age ... we have much to learn from the manuscript age" (Twyman 1987, p5). He proposes that studying the early scribes' use of colour, spacing and other graphical devices can usefully inform the design of today's screen-based texts. And as Blackwell points out in his opening section on 'Typography and text' "most information on computer screen is still presented as text".

It is also sometimes assumed that the pictorial representation of a dynamic process is best presented dynamically. However it can be argued that the comic book convention of using a sequence of static frames is sometimes superior for focusing the viewer's attention on the critical events in a process, rather than using an animated sequence in which key moments may be missed. This is of course not to deny the immense value of the moving and interactive visual image in the right context. The Gapminder charts are a case in point (http://www.gapminder.org). Blackwell usefully includes one of these, but as a static presentation. These diagrams come to life and really tell their story through the clustering of balloons that inflate or deflate as they move about the screen when driven through simulated periods of time.

While designing a tool for engineers to learn about the operation and maintenance of an oil system for an aircraft jet engine, Detlev Fischer devised a series of interactive animations, called 'Cinegrams' to display in diagrammatic form various operating procedures (Fischer and Richards 1995). He used the cinematic techniques of time compression and expansion in one animated sequence to show how the slow accumulation of debris in an oil filter, over an extended period of time, would eventually create a blockage to the oil flow and trigger the opening of a by-pass device in split seconds. Notwithstanding my earlier comment about the potential superiority of the comic strip genre for displaying some time dependant processes this particular Cinegram proved very instructive for the targeted users. There are many other examples one could cite where dynamic picturing of this sort has been deployed to similarly good effect in interactive environments.

Shneinderman also comments that: "Blackwell's approach might be enriched by more discussion of visual representation in functional product design tied to meaningful tasks". An area I have worked in is the pictorial representation of engineering assemblies to show that which is normally hidden from view. Techniques to do this on the printed page include 'ghosting' (making occluding parts appear as if transparent), 'exploding' (showing components separately, set out in dis-assembly order along an axis) and cutting away (taking a slice out of an outer shell to reveal mechanisms beneath). All these three-dimensional picturing techniques were used by, if not actually invented by, Leonardo Da Vinci (Richards 2006). All could be enhanced by interactive viewer control - an area of further fruitful exploration for picturing purposes in technical documentation contexts.

Blackwell's section on 'Pictures' warns us that when considering picturing options to avoid the "resemblance fallacy" pointing out the role that convention plays, even in so called photo-realistic images. He also points out that viewers can be distracted from the message by incidental information in 'realistic' pictures. From my own work in the field I know that technical illustrators' synoptic black and white outline depictions are regarded as best for drawing the viewer's attention to the key features of a pictorial representation. Research in this area has shown that when using linear perspective type drawings the appropriate deployment of lines of varying 'weight', rather than of a single thickness, can have a significant effect on viewers' levels of understanding about what is depicted (Richards, Bussard and Newman 2007). This work was done specifically to determine an 'easy to read' visual representational style when manipulating on the screen images of CAD objects. The most effective convention was shown to be: thin lines for edges where both planes forming the edge are visible and thicker lines for edges where only one plane is visible - that is where an outline edge forms a kind of horizon to the object.

These line thickness conventions appear on the face of it to have little to do with how we normally perceive the world, and Blackwell tells us that: "A good pictorial representation need not simulate visual experience any more than a good painting of a unicorn need resemble an actual unicorn". And some particular representations of unicorns can aid our understanding of how to use semiotic theory to figure out how pictures may be interpreted and, importantly, sometimes misunderstood - as I shall describe in the following.

Blackwell mentions semiotics, almost in passing, however it can help unravel some of the complexities of visual representation. Evelyn Goldsmith uses a Charles Addams cartoon to explain the relevance of the 'syntactic', 'semantic' and 'pragmatic' levels of semiotic analysis when applied to pictures (Goldsmith 1978). The cartoon in question, like many of those by Charles Addams, has no caption. It shows two unicorns standing on a small island in the pouring rain forlornly watching the Ark sailing away into the distance. Goldsmith suggests that most viewers will have little trouble in interpreting the overlapping elements in the scene, for example that one unicorn is standing behind the other, nor any difficulty understanding that the texture gradient of the sea stands for a receding horizontal plane. These represent the syntactic level of interpretation. Most adults will correctly identify the various components of the picture at the semantic level, however Goldsmith proposes that a young child might mistake the unicorns for horses and be happy with 'boat' for the Ark. But at the pragmatic level of interpretation, unless a viewer of the picture is aware of the story of Noah's Ark, the joke will be lost  - the connection will not be made between the scene depicted in the drawing and the scarcity of unicorns. This reinforces the point that one should not assume that the understanding of pictures is straightforward. There is much more to it than a simple matter or recognition. This is especially the case when metaphor is involved in visual representation.

Blackwell's section on 'Visual metaphor' is essentially a critique of the use of "theories of visual metaphor" as an "approach to explaining the conventional Mackintosh/Windows 'desktop' ". His is a convincing argument but there is much more which may be said about the use of visual metaphor - especially to show that which otherwise cannot be pictured. In fact most diagrams employ a kind of spatial metaphor when not depicting physical arrangements, for example when using the branches of a tree to represent relations within a family (Richards 2002). The capability to represent the invisible is the great strength of the visual metaphor, but there are dangers, and here I refer back to semiotics and particularly the pragmatic level of analysis. One needs to know the story to get the picture.

In our parental home, one of the many books much loved by my two brothers and me, was The Practical Encyclopaedia for Children (Odhams circa 1948). In it a double page spread illustration shows the possible evolutionary phases of the elephant. These are depicted as a procession of animals in a primordial swamp cum jungle setting. Starting with a tiny fish and passing to a small aquatic creature climbing out of the water onto the bank the procession progresses on through eight phases of transformation, including the Moeritherium and the Paleomatodon, finishing up with the land-based giant of today's African Elephant. Recently one of my brothers confessed to me that through studying this graphical diorama he had believed as a child that the elephant had a life cycle akin to that of a frog. He had understood that the procession was a metaphor for time. He had just got the duration wrong - by several orders of magnitude. He also hadn't understood that each separate depiction was of a different animal. He had used the arguably more sophisticated concept that it was the same animal at different times and stages in its individual development.

Please forgive the cliché if I say that this anecdote clearly illustrates that there can be more to looking at a picture than meets the eye? Blackwell's essay provides some useful pointers for exploring the possibilities of this fascinating territory of picturing and visual representation in general.   

• Blackwell A 2011 'Visual representation' Interaction-Design.org
• Few S 2010 ' Data visualisation for human perception ' Interaction-Design.org
• Fischer D and Richards CJ 1995 'The presentation of time in interactive animated systems diagrams' In: Earnshaw RA and Vince JA (eds) Multimedia Systems and Applications London: Academic Press Ltd (pp141 - 159). ISBN 0-12-227740-6
• Goldsmith E 1978 An analysis of the elements affecting comprehensibility of illustrations intended as supportive of text PhD thesis (CNAA) Brighton Polytechnic
• Korsa R 2010 ' Commentary on Stephen Few's article : Data visualisation for human perception' Interaction-Design.org Odhams c. 1949 The practical encyclopaedia for children (pp 194 - 195)
• Richards CJ 2002 'The fundamental design variables of diagramming' In: Oliver P, Anderson M and Meyer B (eds) Diagrammatic representation and reasoning London: Springer Verlag (pp 85 - 102) ISBN 1-85233-242-5
• Richards CJ 2006 'Drawing out information - lines of communication in technical illustration' Information Design Journal 14 (2) 93 - 107
• Richards CJ, Bussard N, Newman R 2007 'Weighing up line weights: the value of differing line thicknesses in technical illustrations' Information Design Journal 15 (2) 171 - 181
• Shneiderman B 2011 'Commentary on Alan Blackwell's article: Visual representation' Interaction-Design.org
• Twyman M 1982 'The graphic representation of language' Information Design Journal 3 (1) 2 - 22

5.12 Commentary by Peter C-H. Cheng

Alan Blackwell has provided us with a fine introduction to the design of visual representations. The article does a great job in motivating the novice designer of visual representations to explore some of the fundamental issues that lurk just beneath the surface of creating effective representations.  Furthermore, he gives us all quite a challenge:

Alan, quite rightly, claims that we must consider the fundamental principles of symbolic correspondence, if we are to design new genres of visual representations beyond the common forms of displays and interfaces.  The report begins to equip the novice visual representation designer with an understanding of the nature of symbolic correspondence between the components of visual representations and the things they represent, whether objects, actions or ideas.  In particular, it gives a useful survey of how correspondence works in a range of representations and provides a systematic framework of how systems of correspondence can be applied to design. The interactive screen shot is an exemplary visual representation that vividly reveals the correspondence techniques used in each part of the example diagram.

However, suppose you really wished to rise to the challenge of creating novel visual representations, how far will a knowledge of the fundamentals of symbolic correspondence take you? Drawing on my studies of the role of diagrams in the history of science, experience of inventing novel visual representations and research on problem solving and learning with diagrams, from the perspective of Cognitive Science, my view is that such knowledge will be necessary but not sufficient for your endeavours.  So, what else should the budding visual representation designer consider? From the perspective of cognitive science there are at least three aspects that we may profitably target.

First, there is the knowledge of how human process information; specifically the nature of the human cognitive architecture. By this, I mean more than visual perception, but an understanding of how we mentally receive, store, retrieve, transform and transmit information. The way the mind deals with each of these basic types of information processing provides relevant constrains for the design of visual representations. For instance, humans often, perhaps even typically, encode concepts in the form of hierarchies of schemas, which are information structures that coordinate attributes that describe and differentiate classes of concepts. These hierarchies of schemas underpin our ability to efficiently generalize or specialize concepts. Hence, we can use this knowledge to consider whether particular forms of symbolic correspondence will assist or hinder the forms of inference that we hope the user of the representation may make. For example, are the main symbolic correspondences in a visual representation consistent with the key attributes of the schemas for the concepts being considered?

Second, it may be useful for the designer to consider the broader nature of the tasks that the user may wish to do with the designed representation.  Resource allocation, optimization, calculating quantities, inferences about of possible outcomes, classification, reasoning about extreme or special cases, and debugging: these are just a few of the many possibilities. These tasks are more generic than the information-oriented options considered in the 'design uses' column of Figure 27 in the article. They are worth addressing, because they provide constraints for the initial stages of representation design, by narrowing the search for what are likely to be effective correspondences to adopt. For example, if taxonomic classification is important, then separation and layering will be important correspondences; whereas magnitude calculations may demand scale mapping, Euclidian and metrical correspondences.

The third aspect concerns situations in which the visual representation must support not just a single task, but many diverse tasks. For example, a visual representation to help students learn about electricity will be used to explain the topology of circuits, make computations with electrical quantities, provide explanations of circuit behaviour (in terms of formal algebraic models and as qualitative causal models), facilitate fault finding or trouble shooting, among other activities. The creation of novel representations in such circumstances is perhaps one of the most challenging for designers. So, what knowledge can help? In this case, I advocate attempting to design representations on the basis of an analysis of the underlying conceptual structure of the knowledge of the target domain. Why? Because the nature of the knowledge is invariant across different classes of task. For example, for problem solving and learning of electricity, all the tasks depend upon the common fundamental conceptual structures of the domain that knit together the laws governing the physical properties of electricity and circuit topology. Hence, a representation that makes these concepts readily available through effective representation designed will probably be effective for a wide range of tasks.

In summary, it is desirable for the aspiring visual representation designer to consider symbolic correspondence, but I recommend they cast their net more widely for inspiration by learning about the human cognitive architecture, focusing on the nature of the task for which they are designing, and most critically thinking about the underlying conceptual structure of the knowledge of the target domain.

5.13 Commentary by Brad A. Myers

I have been teaching human-computer interaction to students with a wide range of backgrounds for many years. One of the most difficult areas for them to learn seems to be visual design. Students seem to quickly pick up rules like Nielsen's Heuristics for interaction (Nielsen & Molich, 1990), whereas the guidelines for visual design are much more subtle. Alan Blackwell's article presents many useful points, but a designer needs to know so much more! Whereas students can achieve competence at achieving Nielsen's "consistency and standards," for example, they struggle with selecting an appropriate representation for their information. And only a trained graphic designer is likely to be able to create an attractive and effective icon. Some people have a much better aesthetic sense, and can create much more beautiful and appropriate representations. A key goal of my introductory course, therefore, is to try to impart to the students how difficult it is to do visual design, and how wide the set of choices is. Studying the examples that Blackwell provides will give the reader a small start towards effective visual representations, but the path requires talent, study, and then iterative design and testing to evaluate and improve a design's success.

• Nielsen, J., & Molich, R. (1990). Heuristic evaluation of user interfaces. Paper presented at the Proc. ACM CHI'90 Conf, Seattle, WA, 249-256.
• See also: http://www.useit.com/papers/heuristic/heuristic_list.html

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Computer Science > Neural and Evolutionary Computing

Title: time-dependent vae for building latent factor from visual neural activity with complex dynamics.

Abstract: Seeking high-quality neural latent representations to reveal the intrinsic correlation between neural activity and behavior or sensory stimulation has attracted much interest. Currently, some deep latent variable models rely on behavioral information (e.g., movement direction and position) as an aid to build expressive embeddings while being restricted by fixed time scales. Visual neural activity from passive viewing lacks clearly correlated behavior or task information, and high-dimensional visual stimulation leads to intricate neural dynamics. To cope with such conditions, we propose Time-Dependent SwapVAE, following the approach of separating content and style spaces in Swap-VAE, on the basis of which we introduce state variables to construct conditional distributions with temporal dependence for the above two spaces. Our model progressively generates latent variables along neural activity sequences, and we apply self-supervised contrastive learning to shape its latent space. In this way, it can effectively analyze complex neural dynamics from sequences of arbitrary length, even without task or behavioral data as auxiliary inputs. We compare TiDe-SwapVAE with alternative models on synthetic data and neural data from mouse visual cortex. The results show that our model not only accurately decodes complex visual stimuli but also extracts explicit temporal neural dynamics, demonstrating that it builds latent representations more relevant to visual stimulation.

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